diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index bc9ae3e534..8641d9501a 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -12,6 +12,8 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
+        with:
+          python-version: '3.12.0'
       - uses: pre-commit/action@v3.0.0
       - uses: crate-ci/typos@master
         with:
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 55760df5b1..0a1f6e449e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,6 +3,7 @@ repos:
     rev: v4.4.0
     hooks:
       - id: trailing-whitespace
+        exclude_types: ['markdown']
       - id: end-of-file-fixer
         exclude_types: ['jupyter']
       - id: check-yaml
@@ -64,7 +65,10 @@ repos:
     rev: 0.7.16
     hooks:
     - id: mdformat
-      exclude: ^warehouse/(?!README.md)
+      # list of exclusions: https://stackoverflow.com/a/75560858
+      # mdformat does not play nice with GitHub callouts: https://github.com/orgs/community/discussions/16925
+      # so skip README files that use them
+      exclude: 'README.md|warehouse/.*'
       args: ["--number"]
       additional_dependencies:
       - mdformat-gfm
diff --git a/README.md b/README.md
index 600802fbd9..0effb92590 100644
--- a/README.md
+++ b/README.md
@@ -6,8 +6,8 @@ Documentation for this codebase lives at [docs.calitp.org/data-infra](https://do
 
 ## Repository Structure
 
-- [./airflow](./airflow) contains the local dev setup and source code for Airflow DAGs (i.e. ETL)
-- [./ci](./ci) contains continuous integration and deployment scripts using GitHub actions.
+- [./airflow](./airflow) contains the local dev setup and source code for Airflow DAGs (i.e. ETL).
+- [./ci](./ci) contains continuous integration and deployment scripts using GitHub Actions.
 - [./docs](./docs) builds the [docs site](https://docs.calitp.org/data-infra).
 - [./kubernetes](./kubernetes) contains helm charts, scripts and more for deploying apps/services (e.g. Metabase, JupyterHub) on our kubernetes cluster.
 - [./images](./images) contains images we build and deploy for use by services such as JupyterHub.
@@ -16,22 +16,25 @@ Documentation for this codebase lives at [docs.calitp.org/data-infra](https://do
 
 ## Contributing
 
-- Follow the [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/) standard for all commits
-- Use Conventional Commit format for PR titles
-- Use GitHub's *draft* status to indicate PRs that are not ready for review/merging
-- Do not use GitHub's "update branch" button or merge the `main` branch back into a PR branch to update it. Instead, rebase PR branches to update them and resolve any merge conflicts.
-- We use GitHub's "code owners" functionality to designate a person or group of people who are in the line of approval for changes to some parts of this repository - if one or more people are automatically tagged as reviewers by GitHub when you create a PR, an approving review from at least one of them is required to merge. This does not automatically place the PR review in somebody's list of priorities, so please reach out to a reviewer to get eyes on your PR if it's time-sensitive.
+### Pre-commit
+
+This repository uses pre-commit hooks to format code, including [Black](https://black.readthedocs.io/en/stable/index.html). This ensures baseline consistency in code formatting.
+
+> [!IMPORTANT]  
+> Before contributing to this project, please install pre-commit locally by running `pip install pre-commit` and `pre-commit install` in the root of the repo. 
 
-## Linting and type-checking
+Once installed, pre-commit checks will run before you can make commits locally. If a pre-commit check fails, it will need to be addressed before you can make your commit. Many formatting issues are fixed automatically within the pre-commit actions, so check the changes made by pre-commit on failure -- they may have automatically addressed the issues that caused the failure, in which case you can simply re-add the files, re-attempt the commit, and the checks will then succeed. 
 
-### pre-commit
+Installing pre-commit locally saves time dealing with formatting issues on pull requests. There is a [GitHub Action](./.github/workflows/lint.yml)
+that runs pre-commit on all files, not just changed ones, as part of our continuous integration. 
 
-This repository pre-commit hooks to format code, including black. To install
-pre-commit locally, run `pip install pre-commit` & `pre-commit install`
-in the root of the repo. There is a [GitHub Action](./.github/workflows/lint.yml)
-that runs pre-commit on all files, not just changed ones. sqlfluff is currently
-disabled in the CI run due to flakiness, but it will still lint any SQL files
-you attempt to commit locally.
+> [!NOTE]  
+> [SQLFluff](https://sqlfluff.com/) is currently disabled in the CI run due to flakiness, but it will still lint any SQL files you attempt to commit locally. You will need to manually correct SQLFluff errors because we found that SQLFluff's automated fixes could be too aggressive and could change the meaning and function of affected code. 
+
+### Pull requests
+- Use GitHub's *draft* status to indicate PRs that are not ready for review/merging
+- Do not use GitHub's "update branch" button or merge the `main` branch back into a PR branch to update it. Instead, rebase PR branches to update them and resolve any merge conflicts.
+- We use GitHub's "code owners" functionality to designate a person or group of people who are in the line of approval for changes to some parts of this repository - if one or more people are automatically tagged as reviewers by GitHub when you create a PR, an approving review from at least one of them is required to merge. This does not automatically place the PR review in somebody's list of priorities, so please reach out to a reviewer to get eyes on your PR if it's time-sensitive.
 
 ### mypy
 
@@ -52,7 +55,7 @@ and `shapely` (until stubs are available, if ever). We recommend including
 comments where additional asserts or other weird-looking code exist to make mypy
 happy.
 
-## Configuration via Environment Variables
+### Configuration via Environment Variables
 
 Generally we try to configure things via environment variables. In the Kubernetes
 world, these get configured via Kustomize overlays ([example](./kubernetes/apps/overlays/gtfs-rt-archiver-v3-prod/archiver-channel-vars.yaml)).
diff --git a/airflow/.env.sample b/airflow/.env.sample
deleted file mode 100644
index aea9c1b807..0000000000
--- a/airflow/.env.sample
+++ /dev/null
@@ -1,2 +0,0 @@
-CALITP_AMPLITUDE_BENEFITS_API_KEY=api-key
-CALITP_AMPLITUDE_BENEFITS_SECRET_KEY=secret-key
diff --git a/airflow/dags/create_external_tables/README.md b/airflow/dags/create_external_tables/README.md
index 59163b22bf..03b7024432 100644
--- a/airflow/dags/create_external_tables/README.md
+++ b/airflow/dags/create_external_tables/README.md
@@ -3,3 +3,46 @@
 Type: [Now / Scheduled](https://docs.calitp.org/data-infra/airflow/dags-maintenance.html)
 
 This DAG orchestrates the creation of [external tables](https://cloud.google.com/bigquery/docs/external-data-sources), which serve as the interface between our raw / parsed data (stored in Google Cloud Storage) and our data warehouse (BigQuery). Most of our external tables are [hive-partitioned](https://cloud.google.com/bigquery/docs/hive-partitioned-loads-gcs).
+
+Here is an annotated example external table YAML file showing what the fields mean:
+
+```yaml
+# throughout this example, <> brackets denote sample content to be filled in based on your use case and should be removed 
+operator: operators.ExternalTable   # the name of the operator; this does not change
+bucket: gs://<your bucket name>    # fill in the name of your source data bucket here
+prefix_bucket: true     # Boolean for whether or not the bucket name should have `test-` appended when you're running from local Airflow (use this if there's a `test-` bucket used for testing)
+post_hook: |    # this is optional; can provide an example query to check that external table was created successfully. this query will run every time the external table DAG runs
+  SELECT *
+  FROM `{{ get_project_id() }}`.<your dataset as defined below under destination_project_dataset_table>.<your table name as defined below under destination_project_dataset_table>
+  LIMIT 1;
+source_objects: # this tells the external table which path & file format to look in for the objects that will be queryable through this external table 
+  - "<the top level folder name within your bucket that should be used for this external table like my_data>/*.<your file extension, most likely '.jsonl.gz'>"     
+destination_project_dataset_table: "<desired dataset name like external_my_data_source>.<desired table name, may be like topic_name__specific_data_name>"   # this defines the external table name (dataset and table name) through which the data will be accessible in BigQuery
+source_format: NEWLINE_DELIMITED_JSON   # file format of raw data; generally should not change -- allowable options are specified here: https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#ExternalDataConfiguration.FIELDS.source_format
+use_bq_client: true     # this option only exists for backwards compatibility; should always be true for new tables
+hive_options:   # this section provides information about how hive-partitioning is used
+  mode: CUSTOM  # options are CUSTOM and AUTO. if CUSTOM, you need to define the hive partitions and their datatypes in the source_uri_prefix below; if you use AUTO, you only need to provide the top-level directory in the source_uri_prefix
+  require_partition_filter: false   # default is true: if true, users will have to provide a filter to query this data; false is usually fine except for very large data like GTFS-RT
+  source_uri_prefix: "<the top level folder name within your bucket that should be used for this external table (should match what's entered in source_objects above)>/{<if CUSTOM under mode above: hive partition name: hive partition data type like 'dt:DATE'>}"    # this tells the hive partitioning where to look. if mode = CUSTOM, should be something like "my_data/{dt:DATE}/{ts:TIMESTAMP}/{some_label:STRING}/" with the entire hive path defined; if mode = AUTO, should be like "my_data/"
+schema_fields:  # here you fill in the schema of the actual files, which will become the schema of the external table
+# make one list item per column that you want to be available in BigQuery
+# if there are columns in the source data that you don't want in BigQuery, you don't have to include them here
+# hive partition path elements (like "date", if present) will be added as columns automatically and should not be specified here
+# if you don't specify a schema, BigQuery will attempt to auto-detect the schema: https://cloud.google.com/bigquery/docs/schema-detect#schema_auto-detection_for_external_data_sources
+  - name: <column_name>     # this should match the key name for this data in the source JSONL file; see https://cloud.google.com/bigquery/docs/schemas#column_names for BQ naming rules
+    mode: <column mode>     # see https://cloud.google.com/bigquery/docs/schemas#modes
+    type: <column data type>    # see https://cloud.google.com/bigquery/docs/schemas#standard_sql_data_types
+  - name: <second_column_name>
+    mode: <second column mode>
+    type: <second column data type>
+```
+
+## Testing
+
+When testing external table creation locally, pay attention to test environment details:
+* Check the `prefix_bucket` setting in your external table DAG task YAML. If `prefix_bucket` is `true`, a local Airflow run will look for a `test-` prefixed bucket and will point the external table at that test data. 
+   * If there is test data in the `test-` bucket with a different schema than you want for the external table (for example, if during ingest development someone was changing individual field data types), that may cause errors and you may need to delete the test data with the outdated schema. 
+   * There will usually be less data present in a `test-` bucket than in production and data that is present may be unrepresentative or out of date.
+* External tables created by local Airflow will be created in the `cal-itp-data-infra-staging` environment. 
+   * If you're trying to test dbt changes that rely on unmerged external tables changes, you can set the `DBT_SOURCE_DATABASE` environment variable to `cal-itp-data-infra-staging`. This will cause the dbt project to use the staging environment's externabl tables. If the staging external tables are pointed at a `test-` buckets (as described in the bullet above), then the dbt project will run on that test data, which may lead to unexpected results. 
+   * For this reason, it is often easier to make external tables updates in one pull request, get that approved and merged, and then make dbt changes once the external tables are already updated in production so you can test on the production source data.
diff --git a/airflow/dags/create_external_tables/ntd_report_validation/external_table_all_ntdreports.yml b/airflow/dags/create_external_tables/ntd_report_validation/external_table_all_ntdreports.yml
new file mode 100644
index 0000000000..748cbe1279
--- /dev/null
+++ b/airflow/dags/create_external_tables/ntd_report_validation/external_table_all_ntdreports.yml
@@ -0,0 +1,412 @@
+operator: operators.ExternalTable
+bucket: gs://calitp-ntd-report-validation
+prefix_bucket: false
+post_hook: |
+  SELECT *
+  FROM `{{ get_project_id() }}`.external_blackcat.all_ntdreports
+  LIMIT 1;
+source_objects:
+  - "all_NTDReporting/*.jsonl.gz"
+destination_project_dataset_table: "external_blackcat.all_ntdreports"
+source_format: NEWLINE_DELIMITED_JSON
+use_bq_client: true
+hive_options:
+  mode: CUSTOM
+  require_partition_filter: false
+  source_uri_prefix: "all_NTDReporting/{year:STRING}/{dt:DATE}/{ts:TIMESTAMP}/"
+schema_fields:
+  - name: reportid
+    type: INTEGER
+  - name: organization
+    type: STRING
+  - name: reportperiod
+    type: INTEGER
+  - name: reportstatus
+    type: STRING
+  - name: reportlastmodifieddate
+    type: INTEGER
+  - name: ntdreportingstationsandmaintenance_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+      - name: Id
+        type: INTEGER
+      - name: ServiceMode
+        type: STRING
+      - name: ReportId
+        type: INTEGER
+      - name: PTOwnedByServiceProvider
+        type: FLOAT
+      - name: PTOwnedByPublicAgency
+        type: FLOAT
+      - name: PTLeasedByServiceProvider
+        type: FLOAT
+      - name: PTLeasedByPublicAgency
+        type: FLOAT
+      - name: DOOwned
+        type: FLOAT
+      - name: DOLeasedByPublicAgency
+        type: FLOAT
+      - name: DOLeasedFromPrivateEntity
+        type: FLOAT
+      - name: LastModifiedDate
+        type: TIMESTAMP
+  - name: ntdtransitassetmanagementa15_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+    - name: Id
+      type: INTEGER
+    - name: FacilityId
+      type: INTEGER
+    - name: ReportId
+      type: INTEGER
+    - name: FacilityName
+      type: STRING
+    - name: PrimaryMode
+      type: STRING
+    - name: FacilityClass
+      type: STRING
+    - name: FacilityType
+      type: STRING
+    - name: YearBuilt
+      type: INTEGER
+    - name: Size
+      type: STRING
+    - name: DOTCapitalResponsibility
+      type: FLOAT
+    - name: OrganizationCapitalResponsibility
+      type: FLOAT
+    - name: ConditionAssessment
+      type: FLOAT
+    - name: ConditionAssessmentDate
+      type: TIMESTAMP
+    - name: SectionOfLargerFacility
+      type: BOOLEAN
+    - name: Latitude
+      type: STRING
+    - name: LatitudeDirection
+      type: STRING
+    - name: Longitude
+      type: STRING
+    - name: LongitudeDirection
+      type: STRING
+    - name: SecondaryMode
+      type: STRING
+    - name: PrivateMode
+      type: STRING
+  - name: ntdassetandresourceinfo_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+    - name: Id
+      type: INTEGER
+    - name: VehicleId
+      type: INTEGER
+    - name: ReportId
+      type: INTEGER
+    - name: VehicleStatus
+      type: STRING
+    - name: Vin
+      type: INTEGER
+    - name: NTDID
+      type: STRING
+    - name: ADAAccess
+      type: BOOLEAN
+    - name: VehicleType
+      type: STRING
+    - name: FuelType
+      type: STRING
+    - name: FundSource
+      type: STRING
+    - name: AverageEstimatedServiceYearsWhenNew
+      type: INTEGER
+    - name: AverageExpirationYearsWhenNew
+      type: INTEGER
+    - name: VehicleYear
+      type: INTEGER
+    - name: UsefulLifeYearsRemaining
+      type: INTEGER
+    - name: VehicleLength
+      type: FLOAT
+    - name: SeatingCapacity
+      type: INTEGER
+    - name: OwnershipType
+      type: STRING
+    - name: ModesOperatedDisplayText
+      type: STRING
+    - name: ModesOperatedFullText
+      type: STRING
+    - name: LastModifiedDate
+      type: TIMESTAMP
+  - name: ntdreportingp10_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+    - name: Id
+      type: INTEGER
+    - name: ReportId
+      type: INTEGER
+    - name: OrgId
+      type: INTEGER
+    - name: UserId
+      type: STRING
+    - name: FirstName
+      type: STRING
+    - name: LastName
+      type: STRING
+    - name: FullName
+      type: RECORD
+      fields:
+      - name: id
+        type: INTEGER
+      - name: Text
+        type: STRING
+      - name: Value
+        type: STRING
+      - name: Group
+        type: STRING
+      - name: BoolValue
+        type: BOOLEAN
+    - name: PrimaryPhone
+      type: STRING
+    - name: Email
+      type: STRING
+    - name: LastModifiedDate
+      type: TIMESTAMP
+  - name: ntdreportingp20_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+    - name: Id
+      type: INTEGER
+    - name: ReportId
+      type: INTEGER
+    - name: ServiceMode
+      type: STRING
+    - name: TypeOfService
+      type: STRING
+    - name: CommitmentDate
+      type: STRING
+    - name: StartDate
+      type: STRING
+    - name: EndDate
+      type: STRING
+    - name: LastModifiedDate
+      type: TIMESTAMP
+  - name: ntdreportingp50_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+    - name: Id
+      type: INTEGER
+    - name: ReportId
+      type: INTEGER
+    - name: Mode
+      type: RECORD
+      fields:
+      - name: id
+        type: STRING
+      - name: Text
+        type: STRING
+      - name: Value
+        type: STRING
+      - name: Group
+        type: STRING
+      - name: BoolValue
+        type: BOOLEAN
+    - name: Type
+      type: RECORD
+      fields:
+      - name: id
+        type: STRING
+      - name: Text
+        type: STRING
+      - name: Value
+        type: STRING
+      - name: Group
+        type: STRING
+      - name: BoolValue
+        type: BOOLEAN
+    - name: WebLink
+      type: STRING
+    - name: FilePath
+      type: STRING
+    - name: LastModifiedDate
+      type: TIMESTAMP
+  - name: ntdreportinga35_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+    - name: Id
+      type: INTEGER
+    - name: ReportId
+      type: INTEGER
+    - name: EquipmentName
+      type: STRING
+    - name: EquipmentId
+      type: INTEGER
+    - name: VehicleType
+      type: STRING
+    - name: PrimaryMode
+      type: STRING
+    - name: SecondaryMode
+      type: STRING
+    - name: TotalVehicles
+      type: STRING
+    - name: UsefulLifeBenchmark
+      type: BOOLEAN
+    - name: YearOfManufacture
+      type: INTEGER
+    - name: TransitAgencyCapitalResponsibility
+      type: FLOAT
+    - name: EstimatedCost
+      type: FLOAT
+    - name: YearDollarsEstimatedCost
+      type: INTEGER
+    - name: UsefulLifeYearsBenchMark
+      type: INTEGER
+    - name: UsefulLifeYearsRemaining
+      type: INTEGER
+    - name: LastModifiedDate
+      type: TIMESTAMP
+  - name: ntdreportingrr20_intercity_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+    - name: Id
+      type: INTEGER
+    - name: ItemId
+      type: INTEGER
+    - name: ReportId
+      type: INTEGER
+    - name: Item
+      type: STRING
+    - name: Type
+      type: STRING
+    - name: OperationsExpended
+      type: FLOAT
+    - name: CapitalExpended
+      type: FLOAT
+    - name: Description
+      type: STRING
+    - name: AnnualVehicleRevMiles
+      type: INTEGER
+    - name: RegularUnlinkedPassengerTrips
+      type: INTEGER
+    - name: LastModifiedDate
+      type: TIMESTAMP
+  - name: ntdreportingrr20_rural_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+    - name: Id
+      type: INTEGER
+    - name: ReportId
+      type: INTEGER
+    - name: Item
+      type: STRING
+    - name: Revenue
+      type: STRING
+    - name: Type
+      type: STRING
+    - name: CssClass
+      type: STRING
+    - name: OperationsExpended
+      type: FLOAT
+    - name: CapitalExpended
+      type: FLOAT
+    - name: Description
+      type: STRING
+    - name: AnnualVehicleRevMiles
+      type: INTEGER
+    - name: AnnualVehicleRevHours
+      type: INTEGER
+    - name: AnnualUnlinkedPassTrips
+      type: INTEGER
+    - name: AnnualVehicleMaxService
+      type: INTEGER
+    - name: SponsoredServiceUPT
+      type: INTEGER
+    - name: Quantity
+      type: INTEGER
+    - name: LastModifiedDate
+      type: TIMESTAMP
+  - name: ntdreportingrr20_urban_tribal_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+    - name: Id
+      type: INTEGER
+    - name: ItemId
+      type: INTEGER
+    - name: ReportId
+      type: INTEGER
+    - name: Item
+      type: STRING
+    - name: OperationsExpended
+      type: FLOAT
+    - name: CapitalExpended
+      type: FLOAT
+    - name: Description
+      type: STRING
+    - name: LastModifiedDate
+      type: TIMESTAMP
+  - name: ntdreportingtamnarrative_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+    - name: Id
+      type: INTEGER
+    - name: ReportId
+      type: INTEGER
+    - name: Type
+      type: STRING
+    - name: Category
+      type: STRING
+    - name: VehiclesInAssetClass
+      type: INTEGER
+    - name: VehiclesExceededULBTAMPlan
+      type: INTEGER
+    - name: TAMPlanGoalsDescription
+      type: STRING
+    - name: VehiclesToBeRetiredBeyondULB
+      type: INTEGER
+    - name: VehiclesPastULBInTAM
+      type: INTEGER
+    - name: LastModifiedDate
+      type: TIMESTAMP
+  - name: ss60_data
+    type: RECORD
+    mode: REPEATED
+    fields:
+    - name: Id
+      type: INTEGER
+    - name: ItemId
+      type: INTEGER
+    - name: ReportId
+      type: INTEGER
+    - name: Item
+      type: STRING
+    - name: Type
+      type: STRING
+    - name: CssClass
+      type: STRING
+    - name: TransitVehicleAssualts
+      type: INTEGER
+    - name: RevenueFacilityAssualts
+      type: INTEGER
+    - name: NonRevenueFacilityAssualts
+      type: INTEGER
+    - name: OtherLocationAssualts
+      type: INTEGER
+    - name: MajorEvents
+      type: INTEGER
+    - name: Fatalities
+      type: INTEGER
+    - name: Injuries
+      type: INTEGER
+    - name: Quantity
+      type: STRING
+    - name: LastModifiedDate
+      type: TIMESTAMP
diff --git a/airflow/dags/ntd_report_from_blackcat/METADATA.yml b/airflow/dags/ntd_report_from_blackcat/METADATA.yml
new file mode 100644
index 0000000000..a6ee5e2843
--- /dev/null
+++ b/airflow/dags/ntd_report_from_blackcat/METADATA.yml
@@ -0,0 +1,18 @@
+description: "Process raw files from a GCS bucket (NTD reports from BlackCat API) directly into BigQuery"
+schedule_interval: "0 10 * * 1" #10 am every Monday
+tags:
+  - ntd, blackcat
+default_args:
+    owner: airflow
+    depends_on_past: False
+    start_date: "2023-10-02"
+    catchup: False
+    email:
+      - "kim.engie@slalom.com"
+      - "christian.suyat@dot.ca.gov"
+      - "katrina.kaiser@dot.ca.gov"
+    email_on_failure: True
+    pool: default_pool
+    concurrency: 50
+wait_for_defaults:
+    timeout: 3600
diff --git a/airflow/dags/ntd_report_from_blackcat/README.md b/airflow/dags/ntd_report_from_blackcat/README.md
new file mode 100644
index 0000000000..18cd55f96d
--- /dev/null
+++ b/airflow/dags/ntd_report_from_blackcat/README.md
@@ -0,0 +1,9 @@
+# `ntd_report_from_blackcat`
+
+Type: [Now|Scheduled](https://docs.calitp.org/data-infra/airflow/dags-maintenance.html)
+
+This DAG orchestrates the publishing and storing of data, in the form of NTD report submissions, first pushing API data into  Google Cloud Storage in the bucket `calitp-ntd-report-validation`.
+
+Another DAG (part of the `create_external_tables` existing DAG) reads the GCS data in BigQuery in the Cal-ITP data warehouse. The job will take the most recent file of each report type (which has all submitted reports by Caltrans 5311 subrecipients) and publish it into BigQuery `external` tables, if it is not yet there. This job uses the Cal-ITP existing infrastructure for creating external tables, outlined [here](https://docs.calitp.org/data-infra/architecture/data.html).
+
+In the event of failure, the job can be rerun without backfilling.
diff --git a/airflow/dags/ntd_report_from_blackcat/all_submitted_for_ntd.yml b/airflow/dags/ntd_report_from_blackcat/all_submitted_for_ntd.yml
new file mode 100644
index 0000000000..53263c8d33
--- /dev/null
+++ b/airflow/dags/ntd_report_from_blackcat/all_submitted_for_ntd.yml
@@ -0,0 +1,7 @@
+operator: operators.BlackCatApiToGCSOperator
+
+bucket: "gs://calitp-ntd-report-validation"
+api_url: "https://services.blackcattransit.com/api/APIModules/GetNTDReportsByYear/BCG_CA/"
+api_tablename_suffix: "NTDReporting"
+form: "all"
+bq_table_name_suffix: "ntdreports"
diff --git a/airflow/dags/ntd_report_publish_validation/METADATA.yml b/airflow/dags/ntd_report_publish_validation/METADATA.yml
new file mode 100644
index 0000000000..f41c5cfe59
--- /dev/null
+++ b/airflow/dags/ntd_report_publish_validation/METADATA.yml
@@ -0,0 +1,18 @@
+description: "Process BigQuery tables into Excel files, save in GCS"
+schedule_interval: "0 20 * * 1" #8 pm every Monday
+tags:
+  - ntd, blackcat
+default_args:
+    owner: airflow
+    depends_on_past: False
+    start_date: "2023-10-02"
+    catchup: False
+    email:
+      - "kim.engie@slalom.com"
+      - "christian.suyat@dot.ca.gov"
+      - "katrina.kaiser@dot.ca.gov"
+    email_on_failure: True
+    pool: default_pool
+    concurrency: 50
+wait_for_defaults:
+    timeout: 3600
diff --git a/airflow/dags/ntd_report_publish_validation/README.md b/airflow/dags/ntd_report_publish_validation/README.md
new file mode 100644
index 0000000000..33fda0cd15
--- /dev/null
+++ b/airflow/dags/ntd_report_publish_validation/README.md
@@ -0,0 +1,7 @@
+# `ntd_report_publish_validation`
+
+Type: [Now|Scheduled](https://docs.calitp.org/data-infra/airflow/dags-maintenance.html)
+
+This DAG orchestrates the publishing of NTD Report validation checks in the form of Excel files, that it saves into Google Cloud Storage. Checks conducted on submitted NTD report submissions, previously stored into BigQuery with dbt models. They are then converted to Excel files and saves in the Google Cloud Storage bucket `calitp-ntd-report-validation`.
+
+In the event of failure, the job can be rerun without backfilling.
diff --git a/airflow/docker-compose.yaml b/airflow/docker-compose.yaml
index 11f86263f7..aeddc2419b 100644
--- a/airflow/docker-compose.yaml
+++ b/airflow/docker-compose.yaml
@@ -63,9 +63,6 @@ x-airflow-common:
     CALITP_BQ_MAX_BYTES: 10000000000
     CALITP_SLACK_URL: ${CALITP_SLACK_URL}
 
-    CALITP_AMPLITUDE_BENEFITS_API_KEY: ${CALITP_AMPLITUDE_BENEFITS_API_KEY}
-    CALITP_AMPLITUDE_BENEFITS_SECRET_KEY: ${CALITP_AMPLITUDE_BENEFITS_SECRET_KEY}
-
     # connections - required to allow local testing of pod operators
     # see https://stackoverflow.com/a/55064944/1144523
     AIRFLOW_CONN_GOOGLE_CLOUD_DEFAULT: "google-cloud-platform://:@:?extra__google_cloud_platform__project=cal-itp-data-infra"
diff --git a/airflow/plugins/operators/__init__.py b/airflow/plugins/operators/__init__.py
index 24fd5fd7ce..9ffc42acf9 100644
--- a/airflow/plugins/operators/__init__.py
+++ b/airflow/plugins/operators/__init__.py
@@ -1,5 +1,6 @@
 # flake8: noqa
 from operators.airtable_to_gcs import AirtableToGCSOperator
+from operators.blackcat_to_gcs import BlackCatApiToGCSOperator
 from operators.external_table import ExternalTable
 from operators.gtfs_csv_to_jsonl import GtfsGcsToJsonlOperator
 from operators.gtfs_csv_to_jsonl_hourly import GtfsGcsToJsonlOperatorHourly
diff --git a/airflow/plugins/operators/blackcat_to_gcs.py b/airflow/plugins/operators/blackcat_to_gcs.py
new file mode 100644
index 0000000000..dcbbc43458
--- /dev/null
+++ b/airflow/plugins/operators/blackcat_to_gcs.py
@@ -0,0 +1,162 @@
+import gzip
+import logging
+import os
+from typing import Optional
+
+import pandas as pd
+import pendulum
+import requests
+from calitp_data_infra.storage import get_fs, make_name_bq_safe
+from pydantic import BaseModel
+
+from airflow.models import BaseOperator
+
+
+def write_to_log(logfilename):
+    """
+    Creates a logger object that outputs to a log file, to the filename specified,
+    and also streams to console.
+    """
+    logger = logging.getLogger(__name__)
+    logger.setLevel(logging.INFO)
+    formatter = logging.Formatter(
+        "%(asctime)s:%(levelname)s: %(message)s", datefmt="%y-%m-%d %H:%M:%S"
+    )
+    file_handler = logging.FileHandler(logfilename)
+    file_handler.setFormatter(formatter)
+    stream_handler = logging.StreamHandler()
+    stream_handler.setFormatter(formatter)
+
+    if not logger.hasHandlers():
+        logger.addHandler(file_handler)
+        logger.addHandler(stream_handler)
+
+    return logger
+
+
+class BlackCatApiExtract(BaseModel):
+    api_url: str
+    form: str
+    api_tablename_suffix: str
+    bq_table_name_suffix: str
+    data: Optional[pd.DataFrame]
+    logger: Optional[logging.Logger]
+    extract_time: Optional[pendulum.DateTime]
+
+    logger = write_to_log("load_bc_apidata_output.log")
+    extract_time = pendulum.now()
+
+    # pydantic doesn't know dataframe type
+    # see https://stackoverflow.com/a/69200069
+    class Config:
+        arbitrary_types_allowed = True
+
+    def fetch_from_bc_api(self):
+        """Download a BlackCat table as a DataFrame.
+
+        Note that BlackCat API reports have rows structured as follows:
+        [{'ReportId': <id>,
+        'Organization': <organization>,
+        'ReportPeriod': <year>,
+        'ReportStatus': <status>,
+        'ReportLastModifiedDate': <timestamp>,
+        '<table_name>': {'Data': [{colname: value, ...}, {colname: value, ...} ...]}},
+        {'ReportId': <id>, ...etc. to the next organization}]
+
+        This function applies renames in the following order.
+            1. rename column names from snakecase to names utilizing underscores
+            2. rename fields
+            3. apply column prefix (to columns not renamed by 1 or 2)
+        """
+
+        self.logger.info(
+            f"Downloading BlackCat data for {self.extract_time.format('YYYY')}_{self.bq_table_name_suffix}."
+        )
+        # will automatically add the current year to the API url so that it ends with "/YYYY".
+        url = self.api_url + self.extract_time.format("YYYY")
+        response = requests.get(url)
+        blob = response.json()
+
+        raw_df = pd.json_normalize(blob)
+        raw_df["ReportLastModifiedDate"] = raw_df["ReportLastModifiedDate"].astype(
+            "datetime64[ns]"
+        )
+
+        self.data = raw_df.rename(make_name_bq_safe, axis="columns")
+        self.logger.info(
+            f"Downloaded {self.bq_table_name_suffix} data for {self.extract_time.format('YYYY')} with {len(self.data)} rows!"
+        )
+
+    def make_hive_path(self, form: str, bucket: str):
+        if not self.extract_time:
+            raise ValueError(
+                "An extract time must be set before a hive path can be generated."
+            )
+        bq_form_name = str.lower(form).replace("-", "")
+        return os.path.join(
+            bucket,
+            f"{bq_form_name}_{self.api_tablename_suffix}",
+            f"year={self.extract_time.format('YYYY')}",
+            f"dt={self.extract_time.to_date_string()}",
+            f"ts={self.extract_time.to_iso8601_string()}",
+            f"{bq_form_name}_{self.bq_table_name_suffix}.jsonl.gz",
+        )
+
+    def save_to_gcs(self, fs, bucket):
+        hive_path = self.make_hive_path(self.form, bucket)
+        self.logger.info(f"Uploading to GCS at {hive_path}")
+        if len(self.data) == 0:
+            self.logger.info(
+                f"There is no data for {self.api_tablename_suffix} for {self.extract_time.format('YYYY')}, not saving anything. Pipeline exiting."
+            )
+            pass
+        else:
+            fs.pipe(
+                hive_path,
+                gzip.compress(self.data.to_json(orient="records", lines=True).encode()),
+            )
+        return hive_path
+
+
+class BlackCatApiToGCSOperator(BaseOperator):
+    template_fields = ("bucket",)
+
+    def __init__(
+        self,
+        bucket,
+        api_url,
+        form,
+        api_tablename_suffix,
+        bq_table_name_suffix,
+        **kwargs,
+    ):
+        """An operator that downloads all data from a BlackCat API
+            and saves it as one JSON file hive-partitioned by date in Google Cloud
+            Storage (GCS). Each org's data will be in 1 row, and for each separate table in the API,
+            a nested column will hold all of it's data.
+
+        Args:
+            bucket (str): GCS bucket where the scraped BlackCat report will be saved.
+            api_url (str): The URL to hit that gets the data. This is dynamically appended with the current year, so that
+             ... in 2023 it will pull data from the ".../2023" url and in 2024, ".../2024" etc.
+            api_tablename_suffix (str): The table that should be extracted from the BlackCat API.
+                MUST MATCH THE API JSON EXACTLY
+            bq_table_name_suffix (str): The table name that will be given in BigQuery. Appears in the GCS bucket path and the filename.
+            form: the NTD form that this report belongs to. E.g., RR-20, A-10, etc. Since it's all forms, here it's "all"
+        """
+        self.bucket = bucket
+        # Instantiating an instance of the BlackCatApiExtract()
+        self.extract = BlackCatApiExtract(
+            api_url=api_url,
+            form=form,
+            api_tablename_suffix=api_tablename_suffix,
+            bq_table_name_suffix=bq_table_name_suffix,
+        )
+
+        super().__init__(**kwargs)
+
+    def execute(self, **kwargs):
+        fs = get_fs()
+        self.extract.fetch_from_bc_api()
+        # inserts into xcoms
+        return self.extract.save_to_gcs(fs, self.bucket)
diff --git a/apps/maps/poetry.lock b/apps/maps/poetry.lock
index 60bed0a916..7ee0459f97 100644
--- a/apps/maps/poetry.lock
+++ b/apps/maps/poetry.lock
@@ -2,111 +2,99 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.8.6"
+version = "3.9.0"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
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+    {file = "aiohttp-3.9.0.tar.gz", hash = "sha256:09f23292d29135025e19e8ff4f0a68df078fe4ee013bca0105b2e803989de92d"},
 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
diff --git a/docs/architecture/data.md b/docs/architecture/data.md
index b2668dc470..710066dc72 100644
--- a/docs/architecture/data.md
+++ b/docs/architecture/data.md
@@ -12,6 +12,8 @@ Some of the key attributes of our approach:
 
 While many of the key elements of our architecture are common to most of our data sources, each data source has some unique aspects as well. [This spreadsheet](https://docs.google.com/spreadsheets/d/1bv1K5lZMnq1eCSZRy3sPd3MgbdyghrMl4u8HvjNjWPw/edit#gid=0) details overviews by data source, outlining the specific code/resources that correspond to each step in the general data flow shown below.
 
+(data-ingest-diagram)=
+
 ## Data ingest diagram
 
 ```{mermaid}
@@ -111,3 +113,46 @@ class first_gcs,second_gcs,validated_gcs gcs_group_boxstyle
 class first_bq,second_bq bq_group_boxstyle
 class gcs_label1,gcs_label2,gcs_label3,bq_label1,bq_label2 group_labelstyle
 ```
+
+## Adding a new data source
+
+Adding a new data source based on the architecture described above involves several steps, outlined below.
+
+```{note}
+If you're bringing in data that is similar to existing data (for example, a new subset of an existing dataset like a new Airtable or Littlepay table), you should follow the existing pattern for that dataset. [This spreadsheet](https://docs.google.com/spreadsheets/d/1bv1K5lZMnq1eCSZRy3sPd3MgbdyghrMl4u8HvjNjWPw/edit#gid=0) gives overviews of some prominent existing data sources, outlining the specific code/resources that correspond to each step in the [general data flow](data-ingest-diagram) for that data source.
+```
+
+### Determine upstream source type
+
+To determine the best storage location for your raw data (especially if it requires manual curation), consult the [Data Collection and Storage Guidance within the Cal-ITP Data Pipeline Google Doc](https://docs.google.com/document/d/1-l6c99UUZ0o3Ln9S_CAt7iitGHvriewWhKDftESE2Dw/edit).
+
+The [Should it be a dbt model?](tool_choice) docs section also has some guidance about when a data pipeline should be created.
+
+### Bring data into Google Cloud Storage
+
+We store our raw, un-transformed data in Google Cloud Storage, usually in perpetuity, to ensure that we can always recover the raw data if needed.
+
+We store data in [hive-partitioned buckets](https://cloud.google.com/bigquery/docs/hive-partitioned-queries#supported_data_layouts) so that data is clearly labeled and partitioned for better performance. We use UTC dates and timestamps in hive paths (for example, for the timestamp of the data extract) for consistency.
+
+You will need to set up a way to bring your raw data into the Cal-ITP Google Cloud Storage environment. Most commonly, we use [Airflow](https://airflow.apache.org/) for this.
+
+The [Airflow README in the data-infra repo](https://github.com/cal-itp/data-infra/tree/main/airflow#readme) has information about how to set up Airflow locally for testing and how the Airflow project is structured.
+
+We often bring data into our environment in two steps, created as two separate Airflow [DAGs](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html):
+
+- **Sync the fully-raw data in its original format:** See for example the changes in the `airflow/dags/sync_elavon` directory in [data-infra PR #2376](https://github.com/cal-itp/data-infra/pull/2376/files) (note: this example is typical in terms of its overall structure and use of Cal-ITP storage classes and methods, but the specifics of how to access and request the upstream data source will vary). We do this to preserve the raw data in its original form. This data might be saved in a `calitp-<your-data-source>-raw` bucket.
+- **Convert the saved raw data into a BigQuery-readable gzipped JSONL file:** See for example the changes in the `airflow/dags/parse_elavon` directory in [data-infra PR #2376](https://github.com/cal-itp/data-infra/pull/2376/files). This prepares the data is to be read into BigQuery. **Conversion here should be limited to the bare minimum needed to make the data BigQuery-compatible, for example converting column names that would be invalid in BigQuery and changing the file type to gzipped JSONL.** This data might be saved in a `calitp-<your-data-source>-parsed` bucket.
+
+```{note}
+When you merge a pull request creating a new Airflow DAG, that DAG will be paused by default. To start the DAG, someone will need to log into [the Airflow UI (requires Composer access in Cal-ITP Google Cloud Platform instance)](https://o1d2fa0877cf3fb10p-tp.appspot.com/home) and unpause the DAG. 
+```
+
+### Create external tables
+
+We use [external tables](https://cloud.google.com/bigquery/docs/external-data-sources#external_tables) to allow BigQuery to query data stored in Google Cloud Storage. External tables do not move data into BigQuery; they simply define the data schema which BigQuery can then use to access the data still stored in Google Cloud Storage.
+
+External tables are created by the [`create_external_tables` Airflow DAG](https://github.com/cal-itp/data-infra/tree/main/airflow/dags/create_external_tables) using the [ExternalTable custom operator](https://github.com/cal-itp/data-infra/blob/main/airflow/plugins/operators/external_table.py). Testing guidance and example YAML for how to create your external table is provided in the [Airflow DAG documentation](https://github.com/cal-itp/data-infra/tree/main/airflow/dags/create_external_tables#create_external_tables).
+
+### dbt modeling
+
+Considerations for dbt modeling are outlined on the [Developing models in dbt](developing-dbt-models) page.
diff --git a/docs/warehouse/developing_dbt_models.md b/docs/warehouse/developing_dbt_models.md
index 1f076822f3..c39f82a807 100644
--- a/docs/warehouse/developing_dbt_models.md
+++ b/docs/warehouse/developing_dbt_models.md
@@ -4,6 +4,26 @@
 
 Information related to contributing to the [Cal-ITP dbt project](https://github.com/cal-itp/data-infra/tree/main/warehouse).
 
+## What is dbt?
+
+[dbt](https://docs.getdbt.com/docs/introduction#the-power-of-dbt) is the tool we use to manage the data models (tables and views) in our BigQuery data warehouse. We use dbt to define the SQL code that creates our data models, to manage dependencies between models, to document models, and to test models. 
+
+The [Cal-ITP dbt project](https://github.com/cal-itp/data-infra/tree/main/warehouse) runs every day, [orchestrated by Airflow](https://github.com/cal-itp/data-infra/tree/main/airflow/dags/transform_warehouse). When the dbt project runs, it refreshes all the data in the warehouse by running all the queries within the project in order based on the dependencies defined between models. For example, if model B depends on model A, the project will execute model A before model B. 
+
+### dbt project structure
+
+The dbt project (specifically [the `models/` directory](https://github.com/cal-itp/data-infra/tree/main/warehouse/models)) is broken out into data processing stages, broadly according to the [standard outlined by dbt](https://docs.getdbt.com/best-practices/how-we-structure/1-guide-overview#guide-structure-overview). You can read dbt documentation on the purpose of [staging](https://docs.getdbt.com/best-practices/how-we-structure/2-staging#staging-models), [intermediate](https://docs.getdbt.com/best-practices/how-we-structure/3-intermediate#intermediate-models), and [mart](https://docs.getdbt.com/best-practices/how-we-structure/4-marts#marts-models) models. 
+
+Project level configuration lives in two separate places:
+* `dbt_project.yml` ([dbt documentation](https://docs.getdbt.com/reference/dbt_project.yml), [our file](https://github.com/cal-itp/data-infra/blob/main/warehouse/dbt_project.yml)): This contains information about the dbt project structure, for example configuring how the folder structure should be interpreted.
+* `profiles.yml` ([dbt documentation](https://docs.getdbt.com/docs/core/connect-data-platform/profiles.yml), [production file (should be used for prod runs only)](https://github.com/cal-itp/data-infra/blob/main/warehouse/profiles.yml), [template for local/testing use](https://github.com/cal-itp/data-infra/tree/main/warehouse#clone-and-install-the-warehouse-project)): This contains information about how the dbt project should connect to our cloud warehouse (BigQuery) and configures settings associated with that database connection, for example, what the query timeout should be.
+
+```{note}
+The relationship between a folder in `warehouse/models/` and a dataset/schema in BigQuery is configured via [dbt_project.yml](https://github.com/cal-itp/data-infra/blob/main/warehouse/dbt_project.yml) in the `models` section. The `warehouse/models/some_data/my_table.sql` file will not correspond to `calitp-data-infra.some_data.my_table` in BigQuery unless configured to do so; all models are created in the `staging` dataset by default (so, without that configuration, the table would be created in `calitp-data-infra.staging.my_table`).
+
+For an example creating a new entry in `dbt_project.yml` for a new mart dataset, see [data-infra PR #3172](https://github.com/cal-itp/data-infra/pull/3172).
+```
+
 ## Resources
 
 - If you have questions specific to our project or you encounter any issues when developing, please bring those questions to the [`#data-warehouse-devs`](https://cal-itp.slack.com/archives/C050ZNDUL21) or [`#data-office-hours`](https://cal-itp.slack.com/archives/C02KH3DGZL7) Cal-ITP Slack channels. Working through questions "in public" helps build shared knowledge that's searchable later on.
@@ -26,11 +46,11 @@ This section describes the high-level mechanics/process of the developer workflo
 **Please read the [next section](developing_dbt_models#modeling-considerations) for things you should consider from the data modeling perspective.**
 ```
 
-To test your work while developing dbt models, you can edit the `.sql` files for your models, save your changes, and then [run the model from the command line](https://github.com/cal-itp/data-infra/tree/main/warehouse#dbt-commands) to execute the SQL you updated.
+To develop dbt models, you can edit the `.sql` files for your models, save your changes, and then [run the model from the command line](https://github.com/cal-itp/data-infra/tree/main/warehouse#dbt-commands) to execute the SQL you updated.
 
 To inspect tables as you are working, the fastest method is usually to run some manual test queries or "preview" the tables in the [BigQuery user interface](https://console.cloud.google.com/bigquery?project=cal-itp-data-infra-staging). You can also use something like [`pandas.read_gbq`](https://pandas.pydata.org/docs/reference/api/pandas.read_gbq.html) to perform example queries in a notebook.
 
-When you run dbt commands locally on JupyterHub, your models will be created in the `cal-itp-data-infra-staging.<your name>_<dbt folder name, like mart_gtfs>` BigQuery dataset. Note that this is in the `cal-itp-data-infra-staging` Google Cloud Platform project, *not* the production `cal-itp-data-infra` project.
+When you run dbt commands locally or on JupyterHub, your models will be created in the `cal-itp-data-infra-staging.<your name>_<dbt folder name, like mart_gtfs>` BigQuery dataset. Note that this is in the `cal-itp-data-infra-staging` Google Cloud Platform project, *not* the production `cal-itp-data-infra` project.
 
 Once your models are working the way you want and you have added all necessary documentation and tests in YAML files ([see below](developing_dbt_models#modeling-considerations) for more on modeling, documentation, and testing considerations), you are ready to merge.
 
diff --git a/images/jupyter-singleuser/poetry.lock b/images/jupyter-singleuser/poetry.lock
index 80c0ce3d00..5693517597 100644
--- a/images/jupyter-singleuser/poetry.lock
+++ b/images/jupyter-singleuser/poetry.lock
@@ -102,111 +102,99 @@ boto3 = ["boto3 (>=1.26.161,<1.26.162)"]
 
 [[package]]
 name = "aiohttp"
-version = "3.8.6"
+version = "3.9.0"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
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+    {file = "aiohttp-3.9.0.tar.gz", hash = "sha256:09f23292d29135025e19e8ff4f0a68df078fe4ee013bca0105b2e803989de92d"},
 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aioitertools"
diff --git a/jobs/gtfs-aggregator-scraper/poetry.lock b/jobs/gtfs-aggregator-scraper/poetry.lock
index d69b6aa9a2..e139a3ebbd 100644
--- a/jobs/gtfs-aggregator-scraper/poetry.lock
+++ b/jobs/gtfs-aggregator-scraper/poetry.lock
@@ -2,111 +2,99 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.8.6"
+version = "3.9.0"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
diff --git a/jobs/gtfs-rt-parser-v2/poetry.lock b/jobs/gtfs-rt-parser-v2/poetry.lock
index 0d8af43d27..0990197ad4 100644
--- a/jobs/gtfs-rt-parser-v2/poetry.lock
+++ b/jobs/gtfs-rt-parser-v2/poetry.lock
@@ -2,111 +2,99 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.8.6"
+version = "3.9.0"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
diff --git a/jobs/gtfs-schedule-validator/poetry.lock b/jobs/gtfs-schedule-validator/poetry.lock
index cb7ec3bd0a..dcb66a97a5 100644
--- a/jobs/gtfs-schedule-validator/poetry.lock
+++ b/jobs/gtfs-schedule-validator/poetry.lock
@@ -2,111 +2,99 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.8.6"
+version = "3.9.0"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.6"
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+    {file = "aiohttp-3.9.0.tar.gz", hash = "sha256:09f23292d29135025e19e8ff4f0a68df078fe4ee013bca0105b2e803989de92d"},
 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
diff --git a/kubernetes/apps/charts/sentry/values.yaml b/kubernetes/apps/charts/sentry/values.yaml
index 41ce47bd82..f7016ea343 100644
--- a/kubernetes/apps/charts/sentry/values.yaml
+++ b/kubernetes/apps/charts/sentry/values.yaml
@@ -102,3 +102,9 @@ sentry:
       enabled: false
     zookeeper:
       enabled: true
+
+  clickhouse:
+    clickhouse:
+      persistentVolumeClaim:
+        dataPersistentVolume:
+          storage: "50Gi"
diff --git a/packages/calitp-data-analysis/poetry.lock b/packages/calitp-data-analysis/poetry.lock
index 444caaaaac..6d1abaefca 100644
--- a/packages/calitp-data-analysis/poetry.lock
+++ b/packages/calitp-data-analysis/poetry.lock
@@ -2,111 +2,99 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.8.6"
+version = "3.9.0"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
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+    {file = "aiohttp-3.9.0.tar.gz", hash = "sha256:09f23292d29135025e19e8ff4f0a68df078fe4ee013bca0105b2e803989de92d"},
 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
diff --git a/packages/calitp-data-infra/poetry.lock b/packages/calitp-data-infra/poetry.lock
index e1026703c6..90c152a9f8 100644
--- a/packages/calitp-data-infra/poetry.lock
+++ b/packages/calitp-data-infra/poetry.lock
@@ -2,111 +2,99 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.8.6"
+version = "3.9.0"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
diff --git a/runbooks/workflow/disk-space.md b/runbooks/workflow/disk-space.md
index 1985b9ba0c..836a081cd5 100644
--- a/runbooks/workflow/disk-space.md
+++ b/runbooks/workflow/disk-space.md
@@ -2,9 +2,38 @@
 
 We have an [alert](https://monitoring.calitp.org/alerting/grafana/Geo72Nf4z/view) that will trigger when any Kubernetes volume is more than 80% full, placing a message in the #data-infra-alerts channel of the Cal-ITP Slack group. The resolution path will depend on the affected service.
 
-> After following any of these specific runbook sections, check the Disk Usage section of the general [Kubernetes dashboard](https://monitoring.calitp.org/d/oWe9aYxmk/1-kubernetes-deployment-statefulset-daemonset-metrics) in Grafana to verify the disk space consumption has decreased to a safe level for any impacted volumes. The alert should also show as resolved in Slack a couple minutes after recovery.
+After following any of these specific runbook sections, check the Disk Usage section of the general [Kubernetes dashboard](https://monitoring.calitp.org/d/oWe9aYxmk/1-kubernetes-deployment-statefulset-daemonset-metrics) in Grafana to verify the disk space consumption has decreased to a safe level for any impacted volumes. The alert should also show as resolved in Slack a couple minutes after recovery.
 
-## ZooKeeper
+## Identifying affected service
+
+In the expanded details for the alert message posted to Slack, look for a line indicating the `persistentvolumeclaim` (PVC), e.g.
+
+```console
+persistentvolumeclaim=sentry-clickhouse-data-sentry-clickhouse-1,
+```
+
+If you're using [Lens](https://k8slens.dev/), you can easily track down what service this PVC is associated with by first opening the **Storage** > **Persistent Volume Claims** section in Lens, then finding or searching for the named PVC. Once found, you can click the associated **Pod** and the **Controlled By** field will tell you what higher-level `Deployment` or `StatefulSet` that pod was created by—telling you which service is affected. The PVC shown above, for example, leads to the `StatefulSet` named `sentry-clickhouse`.
+
+## Possible actions
+
+In any case of disk usage reaching capacity, there are two approaches to resolving the issue:
+
+- Reduce disk usage
+- Increase disk capacity
+
+Some services are designed to be able to operate within finite storage and may need intervention to help them do that, see the [Service-specific additional steps](#service-specific-additional-steps) section below for guidance on some such services.
+
+If however you determine that the growth in disk usage is likely legitimate and consisting of data we generally want to retain, it may just be time to increase the requested disk size for the affected volume.
+
+## Increasing disk capacity
+
+In a pinch, you can edit a PVC directly in Kubernetes to see if that resolves an issue. After a PVC's request size is increased, the pod it is bound to will need to be manually destroyed and then the new requested size will be applied by the cloud provider's storage controlled before the volume gets re-bound to the next instance of the pod. It may be additionally necessary to shell into the pod or node it's hosted on and execute `resize2fs` on the mounted volume to resize the filesystem within the volume to fill the newly available space.
+
+However, it is important to apply the new requested disk capacity value upstream in source control so that future syncs from GitHub to Kubernetes will not reset it. This typically involved identifying the relevant Helm chart and configuration value to change within one of this repositories Helm values YAML files. [PR #3170](https://github.com/cal-itp/data-infra/pull/3170) provides an example of this being done for the Clickhouse data volume.
+
+## Service-specific additional steps
+
+### ZooKeeper
 
 [ZooKeeper](https://zookeeper.apache.org/) is deployed as part of our Sentry Helm chart. While autopurge should be enabled as part of the deployment values, we've had issues with it not working in the past. The following process will remove old logs and snapshots.
 
@@ -12,7 +41,7 @@ We have an [alert](https://monitoring.calitp.org/alerting/grafana/Geo72Nf4z/view
 2. Execute the cleanup script `./opt/bitnami/zookeeper/bin/zkCleanup.sh -n <count>`; `count` must be at least 3.
    1. If the executable does not exist in this location, you can find it with `find . -name zkCleanup.sh`.
 
-## Kafka
+### Kafka
 
 The [Kafka](https://kafka.apache.org/) pods themselves can have unbound disk space usage if they are not properly configured to drop old data quickly enough. This can cascade into a variety of issues, such as [snuba](https://getsentry.github.io/snuba/architecture/overview.html) workers being unable to actually pull events from Kafka, leading to a scenario that cannot recover without intervention. This list of steps is for resetting one particular service, consumer group, and topic, so it may need to be performed multiple times.
 
@@ -20,7 +49,7 @@ The [Kafka](https://kafka.apache.org/) pods themselves can have unbound disk spa
 
 As a temporary measure, you can increase the capacity of the persistent volume of the pod having issues. You can either edit the persistent volume YAML directly, or run `helm upgrade sentry apps/charts/sentry -n sentry -f apps/values/sentry_sensitive.yaml -f apps/charts/sentry/values.yaml --debug` after setting a larger volume size in `values.yaml`. Either way, you will likely have to restart the pod to let the change take effect.
 
-> The rest of these instructions are relevant not just for disk space issues, but also for resolving a common cause of failing consumers. For instance, when Sentry experiences issues for more than a fleeting moment, sometimes internal logging can get backed up when the service recovers. Two Kubernetes workloads, `sentry-snuba-subscription-consumer-events` and `sentry-snuba-subscription-consumer-transactions`, are both particularly susceptible to these sorts of errors, which correspond to a message of "Broker: Offset out of range" when clicking through to the "App Errors" tab for a failing pod in the Google Cloud Console UI.
+The rest of these instructions are relevant not just for disk space issues, but also for resolving a common cause of failing consumers. For instance, when Sentry experiences issues for more than a fleeting moment, sometimes internal logging can get backed up when the service recovers. Two Kubernetes workloads, `sentry-snuba-subscription-consumer-events` and `sentry-snuba-subscription-consumer-transactions`, are both particularly susceptible to these sorts of errors, which correspond to a message of "Broker: Offset out of range" when clicking through to the "App Errors" tab for a failing pod in the Google Cloud Console UI.
 
 1. Check if there are any failing consumer pods in [Workloads](https://console.cloud.google.com/kubernetes/workload?project=cal-itp-data-infra); you can use a failing pod's logs to identify its topic and potentially its consumer group. To resolve the issue, we need to reset the offset for the Kafka consumer groups and topics associated with these workloads.
 2. Access the Kafka service directly via kubectl (in this example, we're logging into the `sentry-kafka` workload via a pod named `sentry-kafka-0`, but any pod for a given Kafka workload will do the trick): `kubectl exec --stdin --tty sentry-kafka-0 -n sentry -- bash`
diff --git a/script/poetry.lock b/script/poetry.lock
index 0384879198..ff27ee0942 100644
--- a/script/poetry.lock
+++ b/script/poetry.lock
@@ -2,111 +2,99 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.8.6"
+version = "3.9.0"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
diff --git a/services/gtfs-rt-archiver-v3/poetry.lock b/services/gtfs-rt-archiver-v3/poetry.lock
index 9a022e5254..172ba0cd6e 100644
--- a/services/gtfs-rt-archiver-v3/poetry.lock
+++ b/services/gtfs-rt-archiver-v3/poetry.lock
@@ -2,111 +2,99 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.8.6"
+version = "3.9.0"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
diff --git a/warehouse/README.md b/warehouse/README.md
index 2754bfd69c..b7a8686cc7 100644
--- a/warehouse/README.md
+++ b/warehouse/README.md
@@ -27,15 +27,14 @@ are already configured/installed.
 
 3. Execute `poetry install` to create a virtual environment and install requirements.
 
-   > \[!NOTE\]
-   >
-   > If you run into an error complaining about graphviz (e.g. `fatal error: 'graphviz/cgraph.h' file not found`); see [pygraphviz#398](https://github.com/pygraphviz/pygraphviz/issues/398).
-   >
-   > ```bash
-   > export CFLAGS="-I $(brew --prefix graphviz)/include"
-   > export LDFLAGS="-L $(brew --prefix graphviz)/lib"
-   > poetry install
-   > ```
+> [!NOTE]  
+> If you run into an error complaining about graphviz (e.g. `fatal error: 'graphviz/cgraph.h' file not found`); see [pygraphviz#398](https://github.com/pygraphviz/pygraphviz/issues/398).
+>
+> ```bash
+> export CFLAGS="-I $(brew --prefix graphviz)/include"
+> export LDFLAGS="-L $(brew --prefix graphviz)/lib"
+> poetry install
+> ```
 
 4. Execute `poetry run dbt deps` to install the dbt dependencies defined in `packages.yml` (such as `dbt_utils`).
 
@@ -60,17 +59,16 @@ are already configured/installed.
 
    See [the dbt docs on profiles.yml](https://docs.getdbt.com/dbt-cli/configure-your-profile) for more background on this file.
 
-   > \[!Note\]
-   >
-   > This default profile template will set a maximum bytes billed of 2 TB; no models should fail with the default lookbacks in our development environment, even with a full refresh. You can override this limit during the init, or change it later by calling init again and choosing to overwrite (or editing the profiles.yml directly).
+> [!NOTE]  
+> This default profile template will set a maximum bytes billed of 2 TB; no models should fail with the default lookbacks in our development environment, even with a full refresh. You can override this limit during the init, or change it later by calling init again and choosing to overwrite (or editing the profiles.yml directly).
+
 
-   > \[!WARNING\]
-   >
-   > If you receive a warning similar to the following, do **NOT** overwrite the file. This is a sign that you do not have a `DBT_PROFILES_DIR` variable available in your environment and need to address that first (see step 5).
-   >
-   > ```text
-   > The profile calitp_warehouse already exists in /data-infra/warehouse/profiles.yml. Continue and overwrite it? [y/N]:
-   > ```
+> [!WARNING]  
+> If you receive a warning similar to the following, do **NOT** overwrite the file. This is a sign that you do not have a `DBT_PROFILES_DIR` variable available in your environment and need to address that first (see step 5).
+>
+> ```text
+> The profile calitp_warehouse already exists in /data-infra/warehouse/profiles.yml. Continue and overwrite it? [y/N]:
+> ```
 
 7. Check whether `~/.dbt/profiles.yml` was successfully created, e.g. `cat ~/.dbt/profiles.yml`. If you encountered an error, you may create it by hand and fill it with the same content:
 
@@ -153,8 +151,7 @@ Once you have performed the setup above, you are good to go run
    2. In the future, you can specify [selections](https://docs.getdbt.com/reference/node-selection/syntax) (via the `-s` or `--select` flags) to run only a subset of models, otherwise this will run *all* the tables.
    3. By default, your very first `run` is a [full refresh](https://docs.getdbt.com/reference/commands/run#refresh-incremental-models) but you'll need to pass the `--full-refresh` flag in the future if you want to change the schema of incremental tables, or "backfill" existing rows with new logic.
 
-> \[!NOTE\]
->
+> [!NOTE]  
 > In general, it's a good idea to run `seed` and `run --full-refresh` if you think your local environment is substantially outdated (for example, if you haven't worked on dbt models in a few weeks but want to create or modify a model). We have macros in the project that prevent a non-production "full refresh" from actually processing all possible data.
 
 Some additional helpful commands:
@@ -181,12 +178,10 @@ If this is your first time using the terminal, we recommend reading "[Learning t
 
 You can enable [displaying hidden folders/files in macOS Finder](https://www.macworld.com/article/671158/how-to-show-hidden-files-on-a-mac.html) but generally, we recommend using the terminal when possible for editing these files. Generally, `nano ~/.dbt/profiles.yml` will be the easiest method for editing your personal profiles file. `nano` is a simple terminal-based text editor; you use the arrows keys to navigate and the hotkeys displayed at the bottom to save and exit. Reading an [online tutorial for using `nano`](https://www.howtogeek.com/howto/42980/the-beginners-guide-to-nano-the-linux-command-line-text-editor/) may be useful if you haven't used a terminal-based editor before.
 
-> \[!Note\]
->
+> [!NOTE]  
 > These instructions assume you are on macOS, but are largely similar for other operating systems. Most \*nix OSes will have a package manager that you should use instead of Homebrew.
 
-> \[!Note\]
->
+> [!NOTE]  
 > If you get `Operation not permitted` when attempting to use the terminal, you may need to [fix your terminal permissions](https://osxdaily.com/2018/10/09/fix-operation-not-permitted-terminal-error-macos/)
 
 ### Install Homebrew (if you haven't)
diff --git a/warehouse/dbt_project.yml b/warehouse/dbt_project.yml
index 61d8c24977..be4c05713f 100644
--- a/warehouse/dbt_project.yml
+++ b/warehouse/dbt_project.yml
@@ -67,3 +67,5 @@ models:
         schema: mart_payments
       benefits:
         schema: mart_benefits
+      ntd_validation:
+        schema: mart_ntd_validation
diff --git a/warehouse/models/docs/_docs_littlepay.md b/warehouse/models/docs/_docs_littlepay.md
index 0d645a2f80..d6f73ec418 100644
--- a/warehouse/models/docs/_docs_littlepay.md
+++ b/warehouse/models/docs/_docs_littlepay.md
@@ -216,6 +216,8 @@ The status of the settlement. Options are:
 * `REJECTED`
 * `SETTLED`
 * `FAILED` - an error occurred during processing
+This field was not added for all agencies until late November 2023, so
+this field will be missing (null) for most rows before then. 
 {% enddocs %}
 
 {% docs lp_request_created_timestamp_utc %}
diff --git a/warehouse/models/intermediate/ntd_validation/int_ntd_a10_facilitiesdata.sql b/warehouse/models/intermediate/ntd_validation/int_ntd_a10_facilitiesdata.sql
new file mode 100644
index 0000000000..5577c2c948
--- /dev/null
+++ b/warehouse/models/intermediate/ntd_validation/int_ntd_a10_facilitiesdata.sql
@@ -0,0 +1,30 @@
+---------
+---- For assessing A10-032: Check that sum of total facilities for each agency, across all modes, is a whole number.
+---------
+
+WITH fac_by_mode as (
+SELECT
+    organization,
+    api_report_period,
+    service_mode,
+    COALESCE(ROUND(pt_owned_by_service_provider,2) ,0)
+    + COALESCE(ROUND(pt_owned_by_public_agency, 2),0)
+    + COALESCE(ROUND(pt_leased_by_public_agency, 2), 0)
+    + COALESCE(ROUND(pt_leased_by_service_provider, 2), 0)
+    + COALESCE(ROUND(do_owned, 2), 0)
+    + COALESCE(ROUND(do_leased_by_public_agency,2), 0)
+    + COALESCE(ROUND(do_leased_from_private_entity, 2), 0)
+    as total_facilities
+FROM {{ ref('stg_ntd_2023_a10') }}
+),
+
+collapsed_fac as (
+    SELECT
+        organization,
+        api_report_period,
+        SUM(total_facilities) as total_facilities
+    FROM fac_by_mode
+    GROUP BY organization, api_report_period
+)
+
+select * from collapsed_fac
diff --git a/warehouse/models/intermediate/ntd_validation/int_ntd_a30_voms_vins_totals.sql b/warehouse/models/intermediate/ntd_validation/int_ntd_a30_voms_vins_totals.sql
new file mode 100644
index 0000000000..a5df5e51a3
--- /dev/null
+++ b/warehouse/models/intermediate/ntd_validation/int_ntd_a30_voms_vins_totals.sql
@@ -0,0 +1,26 @@
+--- get the # of active VINS in the inventory - DON'T HAVE
+--- get the # of VOMS in the rr-20
+-- get the # of vins in the A30
+
+with voms_rr20 as (
+    select organization,
+    fiscal_year,
+    AVG(VOMX) as rr20_voms
+    FROM {{ ref('int_ntd_rr20_service_alldata') }}
+    GROUP BY organization, fiscal_year
+),
+
+vins_a30 as (
+    SELECT organization,
+    api_report_period as fiscal_year,
+    COUNT(DISTINCT VIN) as a30_vin_n
+    FROM {{ ref('stg_ntd_2023_a30_assetandresourceinfo') }}
+    GROUP BY organization, fiscal_year
+)
+
+select voms_rr20.*, vins_a30.a30_vin_n
+FROM voms_rr20
+FULL OUTER JOIN vins_a30
+    ON voms_rr20.organization = vins_a30.organization
+    AND voms_rr20.fiscal_year = vins_a30.fiscal_year
+ORDER BY organization, fiscal_year
diff --git a/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_financial_fare_revenues.sql b/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_financial_fare_revenues.sql
new file mode 100644
index 0000000000..cece952604
--- /dev/null
+++ b/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_financial_fare_revenues.sql
@@ -0,0 +1,59 @@
+-- need fare rev and upt for each year.
+
+WITH fare_rev_2023 as (
+  select
+    organization,
+    api_report_period as fiscal_year,
+    item as mode,
+    operations_expended + capital_expended as Fare_Revenues,
+     from {{ ref('stg_ntd_2023_rr20_rural') }}
+     WHERE type = "Fare Revenues"
+),
+upt_2023 as (
+  select
+    organization,
+    api_report_period as fiscal_year,
+    item as mode,
+    annual_unlinked_pass_trips as Annual_UPT
+    from {{ ref('stg_ntd_2023_rr20_rural') }}
+     WHERE type = "Service Data"
+),
+all_2023 as (
+  select fare_rev_2023.organization,
+    fare_rev_2023.fiscal_year,
+    fare_rev_2023.mode,
+    fare_rev_2023.Fare_Revenues,
+    upt_2023.Annual_UPT
+  FROM fare_rev_2023
+  FULL OUTER JOIN upt_2023
+    ON fare_rev_2023.organization = upt_2023.organization
+    AND fare_rev_2023.mode = upt_2023.mode
+),
+fare_rev_2022 as (
+  SELECT Organization_Legal_Name as organization,
+  Fiscal_Year as fiscal_year,
+  sum(Fare_Revenues) as Fare_Revenues
+  FROM {{ ref('stg_ntd_2022_rr20_financial') }}
+  GROUP BY organization, fiscal_year
+),
+upt_2022 as (
+  select
+    Organization_Legal_Name as organization,
+    Fiscal_Year as  fiscal_year,
+    Mode as mode,
+    Annual_UPT
+from {{ ref('stg_ntd_2022_rr20_service') }}
+),
+all_2022 as (
+  select fare_rev_2022.organization, fare_rev_2022.fiscal_year,
+    upt_2022.Mode, fare_rev_2022.Fare_Revenues, upt_2022.Annual_UPT
+  FROM fare_rev_2022
+  FULL OUTER JOIN upt_2022
+    ON fare_rev_2022.organization = upt_2022.organization
+)
+
+SELECT * from all_2023
+
+UNION ALL
+
+SELECT * from all_2022
diff --git a/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_financial_specific_funds.sql b/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_financial_specific_funds.sql
new file mode 100644
index 0000000000..768ca69c44
--- /dev/null
+++ b/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_financial_specific_funds.sql
@@ -0,0 +1,50 @@
+-------
+-- NTD validation errors about these 1 specific funding sources.
+--- ID #s RR20F-070, RR20F-065, RR20F-068, RR20F-066, RR20F-013. Sums the capital expenses across all funding sources
+--- In 2022 the data is a different format than 2023 **and onwards**.
+--- Only needed for the 2023 error checking (to compare to "last year"). In 2024 you don't need 2022 data.
+-------
+
+WITH longform_2023 AS (
+    SELECT
+    organization,
+    api_report_period AS fiscal_year,
+    operations_expended + capital_expended AS total_expended,
+    REPLACE(
+      REPLACE(
+        REPLACE(item, 'FTA Formula Grants for Rural Areas (§5311)', 'FTA_Formula_Grants_for_Rural_Areas_5311'),
+        'Other Directly Generated Funds', 'Other_Directly_Generated_Funds'),
+    'Local Funds', 'Local_Funds') as item
+     FROM {{ ref('stg_ntd_2023_rr20_rural') }}
+     WHERE item LIKE "%Directly Generated Funds%"
+      OR item LIKE "%Formula Grants for Rural Areas%"
+      OR item LIKE "Local Funds"
+),
+wide_2023 AS (
+    SELECT * FROM
+    (SELECT * FROM longform_2023)
+    PIVOT(AVG(total_expended) FOR item IN ('FTA_Formula_Grants_for_Rural_Areas_5311', 'Other_Directly_Generated_Funds', 'Local_Funds'))
+    ORDER BY organization
+),
+data_2022 AS (
+    SELECT Organization_Legal_Name as organization,
+        Fiscal_Year as fiscal_year,
+        SUM(Other_Directly_Generated_Funds) as Other_Directly_Generated_Funds_2022,
+        SUM(FTA_Formula_Grants_for_Rural_Areas_5311) as FTA_Formula_Grants_for_Rural_Areas_5311_2022,
+        Null as Local_Funds_2022
+    FROM {{ ref('stg_ntd_2022_rr20_financial') }}
+    GROUP BY 1,2 -- noqa: L054
+    ORDER BY organization
+)
+
+select wide_2023.organization,
+    wide_2023.FTA_Formula_Grants_for_Rural_Areas_5311 as FTA_Formula_Grants_for_Rural_Areas_5311_2023,
+    wide_2023.Other_Directly_Generated_Funds as Other_Directly_Generated_Funds_2023,
+    wide_2023.Local_Funds as Local_Funds_2023,
+    data_2022.FTA_Formula_Grants_for_Rural_Areas_5311_2022,
+    data_2022.Other_Directly_Generated_Funds_2022,
+    data_2022.Local_Funds_2022
+from wide_2023
+FULL OUTER JOIN data_2022
+    ON wide_2023.organization = data_2022.organization
+ORDER BY organization
diff --git a/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_financial_total_exp.sql b/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_financial_total_exp.sql
new file mode 100644
index 0000000000..164a3c8403
--- /dev/null
+++ b/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_financial_total_exp.sql
@@ -0,0 +1,54 @@
+------
+--- Compiles data for RR-20 Financial checks on total amounts (operating and capital)
+--- into one table for downstream validation checks. "Prior year" data not needed
+--- NTD error ID #s RR20F-001OA, RR20F-001C, RR20F-182
+------
+
+WITH total_operations_exp_2023 as(
+    select organization,
+    api_report_period as fiscal_year,
+    sum(operations_expended) as Total_Annual_Op_Expenses_by_Mode
+    from {{ ref('stg_ntd_2023_rr20_rural') }}
+     WHERE css_class = "expense"
+     group by organization, api_report_period
+),
+total_capital_exp_bymode_2023 as (
+    select organization,
+    api_report_period as fiscal_year,
+    sum(capital_expended) as Total_Annual_Cap_Expenses_byMode
+    from {{ ref('stg_ntd_2023_rr20_rural') }}
+     WHERE css_class = "expense"
+     group by organization, api_report_period
+),
+total_operations_rev_2023 as (
+    select organization,
+    api_report_period as fiscal_year,
+    sum(operations_expended) as Total_Annual_Op_Revenues_Expended
+    from {{ ref('stg_ntd_2023_rr20_rural') }}
+     WHERE css_class = "revenue"
+     group by organization, api_report_period
+),
+total_cap_exp_byfunds_2023 as (
+    select organization,
+    api_report_period as fiscal_year,
+    sum(capital_expended) as Total_Annual_Cap_Expenses_byFunds
+    from {{ ref('stg_ntd_2023_rr20_rural') }}
+    WHERE css_class = "revenue"
+     group by organization, api_report_period
+)
+
+SELECT
+    total_operations_exp_2023.*,
+    total_capital_exp_bymode_2023.Total_Annual_Cap_Expenses_byMode,
+    total_operations_rev_2023.Total_Annual_Op_Revenues_Expended,
+    total_cap_exp_byfunds_2023.Total_Annual_Cap_Expenses_byFunds
+FROM total_operations_exp_2023
+FULL OUTER JOIN total_capital_exp_bymode_2023
+    ON total_operations_exp_2023.organization = total_capital_exp_bymode_2023.organization
+    AND total_operations_exp_2023.fiscal_year = total_capital_exp_bymode_2023.fiscal_year
+FULL OUTER JOIN total_operations_rev_2023
+    ON total_operations_exp_2023.organization = total_operations_rev_2023.organization
+    AND total_operations_exp_2023.fiscal_year = total_operations_rev_2023.fiscal_year
+FULL OUTER JOIN total_cap_exp_byfunds_2023
+    ON total_operations_exp_2023.organization = total_cap_exp_byfunds_2023.organization
+    AND total_operations_exp_2023.fiscal_year = total_cap_exp_byfunds_2023.fiscal_year
diff --git a/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_service_alldata.sql b/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_service_alldata.sql
new file mode 100644
index 0000000000..0b40f6487c
--- /dev/null
+++ b/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_service_alldata.sql
@@ -0,0 +1,125 @@
+------
+--- Compiles data for RR-20 Service checks from all years into one table for future computation
+------
+
+--- The 2022 data was *not* from the API and so formatted differently
+--- We are *assuming* that data in 2024 and onwards will be the same format as 2023
+--- If you get errors in 2024, check which columns may differ and read errors carefully.
+
+---TO DO: insert parameter for loop, for each year, do what 2023 is doing,
+--- and at the end, add another union statement
+with service_2023 as (
+    select
+    organization,
+    api_report_period as fiscal_year,
+    item as mode,
+    annual_vehicle_rev_miles as Annual_VRM,
+    annual_vehicle_rev_hours as Annual_VRH,
+    annual_unlinked_pass_trips as Annual_UPT,
+    sponsored_service_upt as Sponsored_UPT,
+    annual_vehicle_max_service as VOMX
+    from {{ ref('stg_ntd_2023_rr20_rural') }}
+    WHERE type = "Service Data"
+),
+
+expenses_2023 as (
+    select
+    organization,
+    api_report_period as fiscal_year,
+    item as mode,
+    operations_expended as Total_Annual_Expenses_By_Mode
+    from {{ ref('stg_ntd_2023_rr20_rural') }}
+    WHERE type = "Expenses by Mode"
+),
+
+fare_rev_2023 as (
+    select
+    organization,
+    api_report_period as fiscal_year,
+    sum(operations_expended) as Fare_Revenues
+    from {{ ref('stg_ntd_2023_rr20_rural') }}
+    WHERE type = "Fare Revenues"
+    GROUP BY organization, fiscal_year
+),
+
+all_2023 as (
+    SELECT DISTINCT
+        service_2023.organization,
+        service_2023.fiscal_year,
+        service_2023.mode,
+        expenses_2023.Total_Annual_Expenses_By_Mode,
+        service_2023.Annual_VRM,
+        service_2023.Annual_VRH,
+        service_2023.Annual_UPT,
+        service_2023.Sponsored_UPT,
+        service_2023.VOMX,
+        fare_rev_2023.Fare_Revenues
+    FROM service_2023
+    FULL OUTER JOIN expenses_2023
+        ON service_2023.organization = expenses_2023.organization
+        AND service_2023.fiscal_year = expenses_2023.fiscal_year
+        AND service_2023.mode = expenses_2023.mode
+    FULL OUTER JOIN fare_rev_2023
+        ON service_2023.organization = fare_rev_2023.organization
+        AND service_2023.fiscal_year = fare_rev_2023.fiscal_year
+),
+
+service2022 as (
+    select
+    Organization_Legal_Name as organization,
+    Fiscal_Year as  fiscal_year,
+    Mode as mode,
+    Annual_VRM,
+    Annual_VRH,
+    Annual_UPT,
+    Sponsored_UPT,
+    VOMX
+    from {{ ref('stg_ntd_2022_rr20_service') }}
+),
+
+expenses2022 as (
+    select
+    Organization_Legal_Name as organization,
+    Fiscal_Year as  fiscal_year,
+    Mode as mode,
+    Total_Annual_Expenses_By_Mode
+    FROM {{ ref('stg_ntd_2022_rr20_exp_by_mode') }}
+    WHERE Operating_Capital = "Operating"
+),
+
+fare_rev_2022 as (
+    select
+    Organization_Legal_Name as organization,
+    Fiscal_Year as  fiscal_year,
+    Fare_Revenues
+    FROM {{ ref('stg_ntd_2022_rr20_financial') }}
+    WHERE Operating_Capital = "Operating"
+),
+
+all_2022 as (
+    SELECT DISTINCT
+     service2022.organization,
+        service2022.fiscal_year,
+        service2022.mode,
+        expenses2022.Total_Annual_Expenses_By_Mode,
+        service2022.Annual_VRM,
+        service2022.Annual_VRH,
+        service2022.Annual_UPT,
+        service2022.Sponsored_UPT,
+        service2022.VOMX,
+        fare_rev_2022.Fare_Revenues
+from service2022
+FULL OUTER JOIN expenses2022
+    ON service2022.organization = expenses2022.organization
+    AND service2022.fiscal_year = expenses2022.fiscal_year
+    AND service2022.mode = expenses2022.mode
+INNER JOIN fare_rev_2022
+    ON service2022.organization = fare_rev_2022.organization
+    AND service2022.fiscal_year = fare_rev_2022.fiscal_year
+)
+
+select * FROM all_2022
+
+UNION ALL
+
+select * from all_2023
diff --git a/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_service_ratios.sql b/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_service_ratios.sql
new file mode 100644
index 0000000000..faf9b34570
--- /dev/null
+++ b/warehouse/models/intermediate/ntd_validation/int_ntd_rr20_service_ratios.sql
@@ -0,0 +1,11 @@
+---- NOTE this only works because there is ONE row per org, year, mode.
+---- If this ever changes or gets duplicated upstream, then these calculations will be off.
+
+SELECT
+    *,
+    Total_Annual_Expenses_By_Mode / NULLIF(Annual_VRH,0) as cost_per_hr,
+    Annual_VRM / NULLIF(VOMX,0) as miles_per_veh,
+    Fare_Revenues / NULLIF(Annual_UPT,0) as fare_rev_per_trip,
+    Annual_VRM / NULLIF(Annual_VRH,0) as rev_speed,
+    Annual_UPT / NULLIF(Annual_VRH,0) as trips_per_hr
+FROM {{ ref('int_ntd_rr20_service_alldata') }}
diff --git a/warehouse/models/intermediate/ntd_validation/int_ntd_validation.yml b/warehouse/models/intermediate/ntd_validation/int_ntd_validation.yml
new file mode 100644
index 0000000000..aa8006667d
--- /dev/null
+++ b/warehouse/models/intermediate/ntd_validation/int_ntd_validation.yml
@@ -0,0 +1,29 @@
+version: 2
+
+models:
+  - name: int_ntd_rr20_financial_fare_revenues
+    description: |
+      Setting up the RR-20 data for comparing fare revenues to previous year
+    # tests:
+    #   - dbt_utils.expression_is_true:
+    #       expression: 'status != {{ guidelines_to_be_assessed_status() }}'
+    # columns:
+  - name: int_ntd_rr20_financial_specific_funds
+    description: |
+      Setting up the RR-20 data for comparing specific funding sources - the 5311 funds, and Other directly generated funds
+      For NTD validation error ID #s RR20F-070, RR20F-065, RR20F-068, RR20F-066, RR20F-013
+  - name: int_ntd_rr20_financial_total_exp
+    description: |
+      Setting up the RR-20 data for comparing totals, for operating and capital expenses, reported in different ares of the RR-20
+      For NTD validation error ID #s RR20F-001OA, RR20F-001C, RR20F-182
+  - name: int_ntd_rr20_service_alldata
+    description: |
+      Combines 2023 and 2022 data in preparation for doing NTD validation checks.
+      The 2022 data was *not* from the API and so formatted differently
+      We are *assuming* that data in 2024 and onwards will be the same format as 2023
+      If you get errors in 2024, check which columns may differ and read errors carefully.
+  - name: int_ntd_rr20_service_ratios
+    description: |
+      makes ratios for validation checks
+    config:
+      materialized: table
diff --git a/warehouse/models/mart/ntd_validation/_mart_ntd_validation.yml b/warehouse/models/mart/ntd_validation/_mart_ntd_validation.yml
new file mode 100644
index 0000000000..77e5ccd0a8
--- /dev/null
+++ b/warehouse/models/mart/ntd_validation/_mart_ntd_validation.yml
@@ -0,0 +1,24 @@
+version: 2
+
+models:
+  - name: fct_ntd_rr20_service_checks
+    description: |
+      Runs validation checks on the RR-20 service data. Source data is int_ntd_rr20_service_ratios.
+      This model is still in python but should be converted to SQL as time allows.
+  - name: fct_ntd_a30_vomscheck
+    description: |
+      Runs various checks on VOMS data submitted to NTD, that are also in the file voms_inventory_check.py.
+      Since we don't have the Revenue Inventory table from Black Cat yet, we cannot do all of the checks.
+      TO DO: add when vehicle inventory becomes available.
+  - name: fct_ntd_rr20_equal_totals_check
+    description: |
+      Runs various validation checks that compare total funding amounts reported in different places.
+      For NTD validation error ID #s rr20f_0010a, rr20f_001c
+  - name: fct_ntd_rr20_funds_checks
+    description: |
+      Runs various validation checks on specific RR-20 funding source data.
+      For NTD validation error ID #s rr20f_070, rr20f_066, rr20f_065, rr20f_013, rr20f_068, rr20f_024
+  - name: fct_ntd_a10_facilitiescheck
+    description: |
+      Runs various validation checks on specific A-10 facilities source data.
+      For NTD validation error ID #s a10_032, a10_030
diff --git a/warehouse/models/mart/ntd_validation/fct_ntd_a10_facilitiescheck.sql b/warehouse/models/mart/ntd_validation/fct_ntd_a10_facilitiescheck.sql
new file mode 100644
index 0000000000..3426d07b14
--- /dev/null
+++ b/warehouse/models/mart/ntd_validation/fct_ntd_a10_facilitiescheck.sql
@@ -0,0 +1,43 @@
+-------
+--- Two checks on reported facilities. NTD error ID #s A10-032, A10-030
+-------
+
+WITH a10_032 as (
+    SELECT organization,
+    "A10-032: Whole Number Facilities" as name_of_check,
+    CASE WHEN MOD(CAST(ROUND(total_facilities, 1) as numeric), 1) != 0
+        THEN "Fail"
+        ELSE "Pass"
+    END as check_status,
+    CONCAT("Total facilities = ", CAST(ROUND(total_facilities, 2) AS string)) AS value_checked,
+    CASE WHEN MOD(CAST(ROUND(total_facilities, 1) as numeric), 1) != 0
+        THEN "The reported total facilities do not add up to a whole number. Please explain."
+        ELSE ""
+    END as description,
+    "" as Agency_Response,
+    CURRENT_TIMESTAMP() AS date_checked
+    FROM {{ ref('int_ntd_a10_facilitiesdata') }}
+),
+
+a10_030 as (
+    SELECT organization,
+    "A10-030: Zero Facilities Reported" as name_of_check,
+    CASE WHEN ROUND(total_facilities, 1) = 0
+        THEN "Fail"
+        ELSE "Pass"
+    END as check_status,
+    CONCAT("Total facilities = ", CAST(ROUND(total_facilities, 2) AS string)) AS value_checked,
+    CASE WHEN ROUND(total_facilities, 1) = 0
+        THEN "There are no reported facilities. Please explain."
+        ELSE ""
+    END as description,
+    "" as Agency_Response,
+    CURRENT_TIMESTAMP() AS date_checked
+    FROM {{ ref('int_ntd_a10_facilitiesdata') }}
+)
+
+SELECT * from a10_032
+
+UNION ALL
+
+SELECT * from a10_030
diff --git a/warehouse/models/mart/ntd_validation/fct_ntd_a30_vomscheck.sql b/warehouse/models/mart/ntd_validation/fct_ntd_a30_vomscheck.sql
new file mode 100644
index 0000000000..af06559cee
--- /dev/null
+++ b/warehouse/models/mart/ntd_validation/fct_ntd_a30_vomscheck.sql
@@ -0,0 +1,22 @@
+--- Since we don't have the Revenue Inventory table from Black Cat yet, we cannot do the other checks in the file voms_inventory_check.py.
+--- When we do get that data, we can add them here in their own CTEs, one per check, and then UNION ALL the CTEs together.
+
+WITH rr20f_180 as (
+    SELECT organization,
+    "RR20F-180: VOMS across forms" as name_of_check,
+    CASE WHEN ROUND(rr20_voms, 1) > ROUND(a30_vin_n, 1)
+        THEN "Fail"
+        ELSE "Pass"
+    END as check_status,
+    CONCAT("RR-20 VOMS = ", CAST(ROUND(rr20_voms, 1) AS STRING),
+            "# A-30 VINs = ", CAST(ROUND(a30_vin_n, 1) AS STRING)) AS value_checked,
+    CASE WHEN ROUND(rr20_voms, 1) > ROUND(a30_vin_n, 1)
+        THEN "Total VOMS is greater than total A-30 vehicles reported. Please clarify."
+        ELSE "VOMS & A-30 vehicles reported are equal to and/or lower than active inventory."
+    END as description,
+    "" as Agency_Response,
+    CURRENT_TIMESTAMP() AS date_checked
+    FROM {{ ref('int_ntd_a30_voms_vins_totals') }}
+)
+
+SELECT * from rr20f_180
diff --git a/warehouse/models/mart/ntd_validation/fct_ntd_rr20_equal_totals_check.sql b/warehouse/models/mart/ntd_validation/fct_ntd_rr20_equal_totals_check.sql
new file mode 100644
index 0000000000..bedab61df6
--- /dev/null
+++ b/warehouse/models/mart/ntd_validation/fct_ntd_rr20_equal_totals_check.sql
@@ -0,0 +1,44 @@
+--- We do identical CASE WHEN clauses in each CTE. The results determine 2 different column values but one can only specify 1 col/statement
+
+WITH rr20f_0010a as (
+    select
+    organization,
+    "RR20F-001OA: equal totalsfor operating expenses" as name_of_check,
+    CASE WHEN (ROUND(Total_Annual_Op_Revenues_Expended,0) != ROUND(Total_Annual_Op_Expenses_by_Mode,0))
+        THEN "Fail"
+        ELSE "Pass"
+        END as check_status,
+    CONCAT("Total_Annual_Revenues_Expended = $", CAST(ROUND(Total_Annual_Op_Revenues_Expended,0) AS STRING),
+            ",Total_Annual_Expenses_by_Mode = $", CAST(ROUND(Total_Annual_Op_Expenses_by_Mode,0) AS STRING)) as value_checked,
+    CASE WHEN (ROUND(Total_Annual_Op_Revenues_Expended,0) != ROUND(Total_Annual_Op_Expenses_by_Mode,0))
+        THEN "Total_Annual_Revenues_Expended should, but does not, equal Total_Annual_Expenses_by_Mode. Please provide a narrative justification."
+        ELSE ""
+        END as description,
+    "" as Agency_Response,
+    CURRENT_TIMESTAMP() AS date_checked
+    FROM {{ ref('int_ntd_rr20_financial_total_exp') }}
+),
+rr20f_001c as(
+    select
+    organization,
+    "RR20F-001C: equal totals for capital expenses by mode and funding source expenditures" as name_of_check,
+    CASE WHEN (ROUND(Total_Annual_Cap_Expenses_byMode,0) != ROUND(Total_Annual_Cap_Expenses_byFunds,0))
+        THEN "Fail"
+        ELSE "Pass"
+        END as check_status,
+    CONCAT("Total_Annual_Cap_Expenses_byMode = $", CAST(ROUND(Total_Annual_Cap_Expenses_byMode,0) AS STRING),
+            ",Total_Annual_Cap_Expenses_byFunds = $", CAST(ROUND(Total_Annual_Cap_Expenses_byFunds,0) AS STRING)) as value_checked,
+    CASE WHEN (ROUND(Total_Annual_Cap_Expenses_byMode,0) != ROUND(Total_Annual_Cap_Expenses_byFunds,0))
+        THEN "The sum of Total Expenses for all modes for Uses of Capital does not equal the sum of all values entered for Directly Generated, Non-Federal and Federal Government Funds for Uses of Capital. Please revise or explain."
+        ELSE ""
+        END as description,
+    "" as Agency_Response,
+    CURRENT_TIMESTAMP() AS date_checked
+    FROM {{ ref('int_ntd_rr20_financial_total_exp') }}
+)
+
+SELECT * FROM rr20f_0010a
+
+UNION ALL
+
+SELECT * FROM rr20f_001c
diff --git a/warehouse/models/mart/ntd_validation/fct_ntd_rr20_funds_checks.sql b/warehouse/models/mart/ntd_validation/fct_ntd_rr20_funds_checks.sql
new file mode 100644
index 0000000000..efa6294282
--- /dev/null
+++ b/warehouse/models/mart/ntd_validation/fct_ntd_rr20_funds_checks.sql
@@ -0,0 +1,136 @@
+--- We do identical CASE WHEN clauses in each CTE. The results determine 2 different column values but one can only specify 1 col/statement
+
+WITH rr20f_070 as (
+ select
+    organization,
+    "RR20F-070: 5311 Funds not reported" as name_of_check,
+    CASE WHEN ROUND(FTA_Formula_Grants_for_Rural_Areas_5311_2023) = 0  OR FTA_Formula_Grants_for_Rural_Areas_5311_2023 IS NULL
+        THEN "Fail"
+        ELSE "Pass"
+        END as check_status,
+    CONCAT("2023 = ", CAST(ROUND(FTA_Formula_Grants_for_Rural_Areas_5311_2023,0) AS STRING)) as value_checked,
+    CASE WHEN ROUND(FTA_Formula_Grants_for_Rural_Areas_5311_2023) = 0  OR FTA_Formula_Grants_for_Rural_Areas_5311_2023 IS NULL
+        THEN "The §5311 program is not listed as a revenue source in your report, Please double check and provide a narrative justification."
+        ELSE ""
+        END AS description,
+    "" as Agency_Response,
+    CURRENT_TIMESTAMP() AS date_checked
+    from {{ ref('int_ntd_rr20_financial_specific_funds') }}
+),
+rr20f_066  as (
+    select
+    organization,
+    "RR20F-066: change from zero" as name_of_check,
+    CASE WHEN ((FTA_Formula_Grants_for_Rural_Areas_5311_2023 = 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2023 IS NULL)
+                    AND (FTA_Formula_Grants_for_Rural_Areas_5311_2022 != 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2022 IS NOT NULL))
+                    OR
+                ((FTA_Formula_Grants_for_Rural_Areas_5311_2023 != 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2023 IS NOT NULL)
+                AND (FTA_Formula_Grants_for_Rural_Areas_5311_2022 = 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2022 IS NULL))
+        THEN "Fail"
+        ELSE "Pass"
+        END as check_status,
+    CONCAT("2022 = ", CAST(ROUND(FTA_Formula_Grants_for_Rural_Areas_5311_2022,0) AS STRING),
+            "2023 = ", CAST(ROUND(FTA_Formula_Grants_for_Rural_Areas_5311_2023,0) AS STRING)) as value_checked,
+    CASE WHEN ((FTA_Formula_Grants_for_Rural_Areas_5311_2023 = 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2023 IS NULL)
+                    AND (FTA_Formula_Grants_for_Rural_Areas_5311_2022 != 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2022 IS NOT NULL))
+                    OR
+                ((FTA_Formula_Grants_for_Rural_Areas_5311_2023 != 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2023 IS NOT NULL)
+                AND (FTA_Formula_Grants_for_Rural_Areas_5311_2022 = 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2022 IS NULL))
+        THEN "FTA_Formula_Grants_for_Rural_Areas_5311 funding changed either from or to zero compared to last year. Please provide a narrative justification."
+        ELSE ""
+        END AS description,
+    "" as Agency_Response,
+    CURRENT_TIMESTAMP() AS date_checked
+    from {{ ref('int_ntd_rr20_financial_specific_funds') }}
+),
+rr20f_065 as (
+ select
+    organization,
+    "RR20F-065: 5311 Funds same value" as name_of_check,
+    CASE WHEN (FTA_Formula_Grants_for_Rural_Areas_5311_2023 != 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2023 IS NOT NULL)
+                AND (FTA_Formula_Grants_for_Rural_Areas_5311_2022 != 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2022 IS NOT NULL)
+                AND (FTA_Formula_Grants_for_Rural_Areas_5311_2023 = FTA_Formula_Grants_for_Rural_Areas_5311_2022)
+        THEN "Fail"
+        ELSE "Pass"
+        END as check_status,
+    CONCAT("2022 = ", CAST(ROUND(FTA_Formula_Grants_for_Rural_Areas_5311_2022,0) AS STRING),
+            "2023 = ", CAST(ROUND(FTA_Formula_Grants_for_Rural_Areas_5311_2023,0) AS STRING)) as value_checked,
+    CASE WHEN (FTA_Formula_Grants_for_Rural_Areas_5311_2023 != 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2023 IS NOT NULL)
+                AND (FTA_Formula_Grants_for_Rural_Areas_5311_2022 != 0 OR FTA_Formula_Grants_for_Rural_Areas_5311_2022 IS NOT NULL)
+                AND (FTA_Formula_Grants_for_Rural_Areas_5311_2023 = FTA_Formula_Grants_for_Rural_Areas_5311_2022)
+        THEN "You have identical values for FTA_Formula_Grants_for_Rural_Areas_5311 funding in 2022 and 2023, which is unusual. Please provide a narrative justification."
+        ELSE ""
+        END AS description,
+    "" as Agency_Response,
+    CURRENT_TIMESTAMP() AS date_checked
+    from {{ ref('int_ntd_rr20_financial_specific_funds') }}
+),
+rr20f_013 as (
+    select
+    organization,
+    "RR20F-013: Other Directly Generated Funds same value" as name_of_check,
+    CASE WHEN (Other_Directly_Generated_Funds_2023 != 0 OR Other_Directly_Generated_Funds_2023 IS NOT NULL)
+                AND (Other_Directly_Generated_Funds_2022 != 0 OR Other_Directly_Generated_Funds_2022 IS NOT NULL)
+                AND (Other_Directly_Generated_Funds_2023 = Other_Directly_Generated_Funds_2022)
+        THEN "Fail"
+        ELSE "Pass"
+        END as check_status,
+    CONCAT("2022 = ", CAST(ROUND(Other_Directly_Generated_Funds_2022,0) AS STRING),
+            "2023 = ", CAST(ROUND(Other_Directly_Generated_Funds_2023,0) AS STRING)) as value_checked,
+    CASE WHEN (Other_Directly_Generated_Funds_2023 != 0 OR Other_Directly_Generated_Funds_2023 IS NOT NULL)
+                AND (Other_Directly_Generated_Funds_2022 != 0 OR Other_Directly_Generated_Funds_2022 IS NOT NULL)
+                AND (Other_Directly_Generated_Funds_2023 = Other_Directly_Generated_Funds_2022)
+        THEN "You have identical values for Other_Directly_Generated_Funds funding in 2022 and 2023, which is unusual. Please provide a narrative justification."
+        ELSE ""
+        END AS description,
+    "" as Agency_Response,
+    CURRENT_TIMESTAMP() AS date_checked
+    from {{ ref('int_ntd_rr20_financial_specific_funds') }}
+),
+rr20f_068 as (
+ select
+    organization,
+    "RR20F-068: 5311 Funds rounded to thousand" as name_of_check,
+    CASE WHEN MOD(CAST(ROUND(FTA_Formula_Grants_for_Rural_Areas_5311_2023,0) AS INT),1000) = 0  AND FTA_Formula_Grants_for_Rural_Areas_5311_2023 IS NOT NULL
+        THEN "Fail"
+        ELSE "Pass"
+        END as check_status,
+    CONCAT("2023 = ", CAST(ROUND(FTA_Formula_Grants_for_Rural_Areas_5311_2023,0) AS STRING)) as value_checked,
+    CASE WHEN MOD(CAST(ROUND(FTA_Formula_Grants_for_Rural_Areas_5311_2023,0) AS INT),1000) = 0  AND FTA_Formula_Grants_for_Rural_Areas_5311_2023 IS NOT NULL
+    THEN "FTA_Formula_Grants_for_Rural_Areas_5311 are rounded to the nearest thousand, but should be reported as exact values. Please double check and provide a narrative justification."
+        ELSE ""
+        END AS description,
+    "" as Agency_Response,
+    CURRENT_TIMESTAMP() AS date_checked
+    from {{ ref('int_ntd_rr20_financial_specific_funds') }}
+),
+rr20f_024 as (
+    select
+    organization,
+    "RR20F-024: Local Funds rounded to thousand" as name_of_check,
+    CASE WHEN MOD(CAST(ROUND(Local_Funds_2023) AS INT),1000) = 0  AND Local_Funds_2023 IS NOT NULL
+        THEN "Fail"
+        ELSE "Pass"
+        END as check_status,
+    CONCAT("2023 = ", CAST(ROUND(Local_Funds_2023) AS STRING)) as value_checked,
+    CASE WHEN MOD(CAST(ROUND(Local_Funds_2023) AS INT),1000) = 0 AND Local_Funds_2023 IS NOT NULL
+        THEN "Local Funds are rounded to the nearest thousand, but should be reported as exact values. Please double check and provide a narrative justification."
+        ELSE ""
+        END AS description,
+    "" as Agency_Response,
+    CURRENT_TIMESTAMP() AS date_checked
+    from {{ ref('int_ntd_rr20_financial_specific_funds') }}
+)
+
+SELECT * FROM rr20f_070
+UNION ALL
+SELECT * FROM rr20f_066
+UNION ALL
+SELECT * FROM rr20f_065
+UNION ALL
+SELECT * FROM rr20f_013
+UNION ALL
+SELECT * FROM rr20f_068
+UNION ALL
+SELECT * FROM rr20f_024
+ORDER BY organization
diff --git a/warehouse/models/mart/ntd_validation/fct_ntd_rr20_service_checks.py b/warehouse/models/mart/ntd_validation/fct_ntd_rr20_service_checks.py
new file mode 100644
index 0000000000..4e1a3b8f8f
--- /dev/null
+++ b/warehouse/models/mart/ntd_validation/fct_ntd_rr20_service_checks.py
@@ -0,0 +1,282 @@
+import datetime
+import logging
+
+import pandas as pd
+
+# TO_DO: see if the missing data check can still work or did we already fill it with zeros
+
+
+def write_to_log(logfilename):
+    """
+    Creates a logger object that outputs to a log file, to the filename specified,
+    and also streams to console.
+    """
+    logger = logging.getLogger(__name__)
+    logger.setLevel(logging.INFO)
+    formatter = logging.Formatter(
+        f"%(asctime)s:%(levelname)s: %(message)s",  # noqa: F541, E231
+        datefmt="%y-%m-%d %H:%M:%S",  # noqa: F541, E231
+    )
+    file_handler = logging.FileHandler(logfilename)
+    file_handler.setFormatter(formatter)
+    stream_handler = logging.StreamHandler()
+    stream_handler.setFormatter(formatter)
+
+    if not logger.hasHandlers():
+        logger.addHandler(file_handler)
+        logger.addHandler(stream_handler)
+
+    return logger
+
+
+def check_rr20_ratios(df, variable, threshold, this_year, last_year, logger):
+    """Validation checks where a ratio must be within a certain threshold limit
+    compared to the previous year."""
+    agencies = df["organization"].unique()
+    output = []
+    for agency in agencies:
+        agency_df = df[df["organization"] == agency]
+        logger.info(f"Checking {agency} for {variable} info.")
+        if len(agency_df) > 0:
+            # Check whether data for both years is present
+            if (len(agency_df[agency_df["fiscal_year"] == this_year]) > 0) & (
+                len(agency_df[agency_df["fiscal_year"] == last_year]) > 0
+            ):
+                for mode in agency_df[(agency_df["fiscal_year"] == this_year)][
+                    "mode"
+                ].unique():
+                    value_thisyr = round(
+                        agency_df[
+                            (agency_df["mode"] == mode)
+                            & (agency_df["fiscal_year"] == this_year)
+                        ][variable].unique()[0],
+                        2,
+                    )
+                    if (
+                        len(
+                            agency_df[
+                                (agency_df["mode"] == mode)
+                                & (agency_df["fiscal_year"] == last_year)
+                            ][variable]
+                        )
+                        == 0
+                    ):
+                        value_lastyr = 0
+                    else:
+                        value_lastyr = round(
+                            agency_df[
+                                (agency_df["mode"] == mode)
+                                & (agency_df["fiscal_year"] == last_year)
+                            ][variable].unique()[0],
+                            2,
+                        )
+
+                    if (value_lastyr == 0) and (
+                        abs(value_thisyr - value_lastyr) >= threshold
+                    ):
+                        result = "fail"
+                        check_name = f"{variable}"
+                        mode = mode
+                        description = f"The {variable} for {mode} has changed from last year by > = {threshold * 100}%, please provide a narrative justification."
+                    elif (value_lastyr != 0) and abs(
+                        (value_lastyr - value_thisyr) / value_lastyr
+                    ) >= threshold:
+                        result = "fail"
+                        check_name = f"{variable}"
+                        mode = mode
+                        description = f"The {variable} for {mode} has changed from last year by {round(abs((value_lastyr - value_thisyr) / value_lastyr) * 100, 1)}%, please provide a narrative justification."
+                    else:
+                        result = "pass"
+                        check_name = f"{variable}"
+                        mode = mode
+                        description = ""
+
+                    output_line = {
+                        "Organization": agency,
+                        "name_of_check": check_name,
+                        "mode": mode,
+                        "value_checked": f"{this_year} = {value_thisyr}, {last_year} = {value_lastyr}",
+                        "check_status": result,
+                        "Description": description,
+                    }
+                    output.append(output_line)
+        else:
+            logger.info(f"There is no data for {agency}")
+    checks = pd.DataFrame(output).sort_values(by="Organization")
+    return checks
+
+
+def check_single_number(
+    df,
+    variable,
+    this_year,
+    last_year,
+    logger,
+    threshold=None,
+):
+    """Validation checks where a single number must be within a certain threshold limit
+    compared to the previous year."""
+    agencies = df["organization"].unique()
+    output = []
+    for agency in agencies:
+        if len(df[df["organization"] == agency]) > 0:
+            logger.info(f"Checking {agency} for {variable} info.")
+            # Check whether data for both years is present, if so perform prior yr comparison.
+            if (
+                len(
+                    df[
+                        (df["organization"] == agency)
+                        & (df["fiscal_year"] == this_year)
+                    ]
+                )
+                > 0
+            ) & (
+                len(
+                    df[
+                        (df["organization"] == agency)
+                        & (df["fiscal_year"] == last_year)
+                    ]
+                )
+                > 0
+            ):
+                for mode in df[
+                    (df["organization"] == agency) & (df["fiscal_year"] == this_year)
+                ]["mode"].unique():
+                    value_thisyr = round(
+                        df[
+                            (df["organization"] == agency)
+                            & (df["mode"] == mode)
+                            & (df["fiscal_year"] == this_year)
+                        ][variable].unique()[0],
+                        2,
+                    )
+                    # If there's no data for last yr:
+                    if (
+                        len(
+                            df[
+                                (df["organization"] == agency)
+                                & (df["mode"] == mode)
+                                & (df["fiscal_year"] == last_year)
+                            ][variable]
+                        )
+                        == 0
+                    ):
+                        value_lastyr = 0
+                    else:
+                        value_lastyr = round(
+                            df[
+                                (df["organization"] == agency)
+                                & (df["mode"] == mode)
+                                & (df["fiscal_year"] == last_year)
+                            ][variable].unique()[0],
+                            2,
+                        )
+
+                    if (round(value_thisyr) == 0 and round(value_lastyr) != 0) | (
+                        round(value_thisyr) != 0 and round(value_lastyr) == 0
+                    ):
+                        result = "fail"
+                        check_name = f"{variable}"
+                        mode = mode
+                        description = f"The {variable} for {mode} has changed either from or to zero compared to last year. Please provide a narrative justification."
+                    # run only the above check on whether something changed from zero to non-zero, if no threshold is given
+                    elif threshold is None:
+                        result = "pass"
+                        check_name = f"{variable}"
+                        mode = mode
+                        description = ""
+                        pass
+                    # also check for pct change, if a threshold parameter is passed into function
+                    elif (value_lastyr == 0) and (
+                        abs(value_thisyr - value_lastyr) >= threshold
+                    ):
+                        result = "fail"
+                        check_name = f"{variable}"
+                        mode = mode
+                        description = f"The {variable} for {mode} was 0 last year and has changed by > = {threshold * 100}%, please provide a narrative justification."
+                    elif (value_lastyr != 0) and abs(
+                        (value_lastyr - value_thisyr) / value_lastyr
+                    ) >= threshold:
+                        result = "fail"
+                        check_name = f"{variable}"
+                        mode = mode
+                        description = f"The {variable} for {mode} has changed from last year by {round(abs((value_lastyr - value_thisyr) / value_lastyr) * 100, 1)}%; please provide a narrative justification."  # noqa: E702
+                    else:
+                        result = "pass"
+                        check_name = f"{variable}"
+                        mode = mode
+                        description = ""
+
+                    output_line = {
+                        "Organization": agency,
+                        "name_of_check": check_name,
+                        "mode": mode,
+                        "value_checked": f"{this_year} = {value_thisyr}, {last_year} = {value_lastyr}",
+                        "check_status": result,
+                        "Description": description,
+                    }
+                    output.append(output_line)
+        else:
+            logger.info(f"There is no data for {agency}")
+    checks = pd.DataFrame(output).sort_values(by="Organization")
+    return checks
+
+
+def model(dbt, session):
+    # Set up the logger object
+    logger = write_to_log("rr20_ftc_servicechecks_log.log")
+
+    this_year = datetime.datetime.now().year
+    last_year = this_year - 1
+    this_date = (
+        datetime.datetime.now().date().strftime("%Y-%m-%d")
+    )  # for suffix on Excel files
+
+    # Load data from BigQuery - pass in the dbt model that we draw from.
+    allyears = dbt.ref("int_ntd_rr20_service_ratios")
+    allyears = allyears.toPandas()
+
+    numeric_columns = allyears.select_dtypes(include=["number"]).columns
+    allyears[numeric_columns] = allyears[numeric_columns].fillna(
+        value=0, inplace=False, axis=1
+    )
+
+    # Run validation checks
+    cph_checks = check_rr20_ratios(
+        allyears, "cost_per_hr", 0.30, this_year, last_year, logger
+    )
+    mpv_checks = check_rr20_ratios(
+        allyears, "miles_per_veh", 0.20, this_year, last_year, logger
+    )
+    vrm_checks = check_single_number(
+        allyears, "Annual_VRM", this_year, last_year, logger, threshold=0.30
+    )
+    frpt_checks = check_rr20_ratios(
+        allyears, "fare_rev_per_trip", 0.25, this_year, last_year, logger
+    )
+    rev_speed_checks = check_rr20_ratios(
+        allyears, "rev_speed", 0.15, this_year, last_year, logger
+    )
+    tph_checks = check_rr20_ratios(
+        allyears, "trips_per_hr", 0.30, this_year, last_year, logger
+    )
+    voms0_check = check_single_number(allyears, "VOMX", this_year, last_year, logger)
+
+    # Combine checks into one table
+    rr20_checks = pd.concat(
+        [
+            cph_checks,
+            mpv_checks,
+            vrm_checks,
+            frpt_checks,
+            rev_speed_checks,
+            tph_checks,
+            voms0_check,
+        ],
+        ignore_index=True,
+    ).sort_values(by="Organization")
+
+    logger.info(f"RR-20 service data checks conducted on {this_date} is complete!")
+
+    # Send table to BigQuery
+    return rr20_checks
diff --git a/warehouse/models/staging/ntd_validation/_src_api_externaltable.yml b/warehouse/models/staging/ntd_validation/_src_api_externaltable.yml
new file mode 100644
index 0000000000..6d7ef7d398
--- /dev/null
+++ b/warehouse/models/staging/ntd_validation/_src_api_externaltable.yml
@@ -0,0 +1,11 @@
+version: 2
+
+sources:
+  - name: ntd_report_validation
+    description: |
+      Data from BlackCat API. Each org's data is be in 1 row, and for each separate table in the API,
+            a nested column holds all of it's data.
+    database: "{{ env_var('DBT_SOURCE_DATABASE', var('SOURCE_DATABASE')) }}"
+    schema: external_blackcat
+    tables:
+      - name: all_ntdreports
diff --git a/warehouse/models/staging/ntd_validation/stg_ntd_2022_rr20_exp_by_mode.sql b/warehouse/models/staging/ntd_validation/stg_ntd_2022_rr20_exp_by_mode.sql
new file mode 100644
index 0000000000..2c6bcd5a38
--- /dev/null
+++ b/warehouse/models/staging/ntd_validation/stg_ntd_2022_rr20_exp_by_mode.sql
@@ -0,0 +1,6 @@
+--- One-time data ingest of 2022 data, whose pattern  which will not be repeated in the future
+--- We pull these tables in to use them in later int and fct models
+-- TODO: enumerate columns
+SELECT -- noqa: AM04
+    *
+FROM `cal-itp-data-infra.blackcat_raw.2022_rr20_expenses_by_mode`
diff --git a/warehouse/models/staging/ntd_validation/stg_ntd_2022_rr20_financial.sql b/warehouse/models/staging/ntd_validation/stg_ntd_2022_rr20_financial.sql
new file mode 100644
index 0000000000..6afc02d872
--- /dev/null
+++ b/warehouse/models/staging/ntd_validation/stg_ntd_2022_rr20_financial.sql
@@ -0,0 +1,6 @@
+--- One-time data ingest of 2022 data, whose pattern  which will not be repeated in the future
+--- We pull these tables in to use them in later int and fct models
+-- TODO: enumerate columns
+SELECT -- noqa: AM04
+    *
+FROM `cal-itp-data-infra.blackcat_raw.2022_rr20_financials__2`
diff --git a/warehouse/models/staging/ntd_validation/stg_ntd_2022_rr20_service.sql b/warehouse/models/staging/ntd_validation/stg_ntd_2022_rr20_service.sql
new file mode 100644
index 0000000000..770028f71c
--- /dev/null
+++ b/warehouse/models/staging/ntd_validation/stg_ntd_2022_rr20_service.sql
@@ -0,0 +1,6 @@
+--- One-time data ingest of 2022 data, whose pattern  which will not be repeated in the future
+--- We pull these tables in to use them in later int and fct models
+-- TODO: enumerate columns
+SELECT -- noqa: AM04
+    *
+FROM `cal-itp-data-infra.blackcat_raw.2022_rr20_service_data`
diff --git a/warehouse/models/staging/ntd_validation/stg_ntd_2023_a10.sql b/warehouse/models/staging/ntd_validation/stg_ntd_2023_a10.sql
new file mode 100644
index 0000000000..f6e2c33054
--- /dev/null
+++ b/warehouse/models/staging/ntd_validation/stg_ntd_2023_a10.sql
@@ -0,0 +1,18 @@
+SELECT
+  organization,
+  reportstatus as api_report_status,
+  TIMESTAMP_MILLIS(reportlastmodifieddate) as api_report_last_modified_date,
+  reportperiod as api_report_period,
+  a10.id as id,
+  a10.ReportId as report_id,
+  a10.ServiceMode as service_mode,
+  a10.PTOwnedByServiceProvider as pt_owned_by_service_provider,
+  a10.PTOwnedByPublicAgency as pt_owned_by_public_agency,
+  a10.PTLeasedByPublicAgency as pt_leased_by_public_agency,
+  a10.PTLeasedByServiceProvider as pt_leased_by_service_provider,
+  a10.DOOwned as do_owned,
+  a10.DOLeasedByPublicAgency as do_leased_by_public_agency,
+  a10.DOLeasedFromPrivateEntity as do_leased_from_private_entity,
+  a10.LastModifiedDate as last_modified_date
+FROM {{ source('ntd_report_validation', 'all_ntdreports') }},
+ UNNEST(`ntdreportingstationsandmaintenance_data`) as `a10`
diff --git a/warehouse/models/staging/ntd_validation/stg_ntd_2023_a30_assetandresourceinfo.sql b/warehouse/models/staging/ntd_validation/stg_ntd_2023_a30_assetandresourceinfo.sql
new file mode 100644
index 0000000000..bf685f194a
--- /dev/null
+++ b/warehouse/models/staging/ntd_validation/stg_ntd_2023_a30_assetandresourceinfo.sql
@@ -0,0 +1,27 @@
+SELECT
+  organization,
+  reportstatus as api_report_status,
+  TIMESTAMP_MILLIS(reportlastmodifieddate) as api_report_last_modified_date,
+  reportperiod as api_report_period,
+  a30.id as id,
+  a30.VehicleId as vehicle_id,
+  a30.NTDID as ntd_id,
+  a30.VehicleLength as vehicle_length,
+  a30.FuelType as fuel_type,
+  a30.FundSource as fund_source,
+  a30.ReportId as report_id,
+  a30.AverageEstimatedServiceYearsWhenNew as average_estimated_service_years_when_new,
+  a30.VehicleStatus as vehicle_status,
+  a30.Vin as vin,
+  a30.ADAAccess as ada_access,
+  a30.VehicleType as vehicle_type,
+  a30.AverageExpirationYearsWhenNew as average_expiration_years_when_new,
+  a30.VehicleYear as vehicle_year,
+  a30.UsefulLifeYearsRemaining as useful_life_years_remaining,
+  a30.SeatingCapacity as seating_capacity,
+  a30.OwnershipType as ownership_type,
+  a30.ModesOperatedDisplayText as modes_operated_display_text,
+  a30.ModesOperatedFullText as modes_operated_full_text,
+  a30.LastModifiedDate as last_modified_date
+FROM {{ source('ntd_report_validation', 'all_ntdreports') }},
+ UNNEST(`ntdassetandresourceinfo_data`) as `a30`
diff --git a/warehouse/models/staging/ntd_validation/stg_ntd_2023_rr20_rural.sql b/warehouse/models/staging/ntd_validation/stg_ntd_2023_rr20_rural.sql
new file mode 100644
index 0000000000..f40457e2f0
--- /dev/null
+++ b/warehouse/models/staging/ntd_validation/stg_ntd_2023_rr20_rural.sql
@@ -0,0 +1,23 @@
+SELECT
+  organization,
+  reportstatus as api_report_status,
+  TIMESTAMP_MILLIS(reportlastmodifieddate) as api_report_last_modified_date,
+  reportperiod as api_report_period,
+  ntdreportingrr20_rural_data.id as id,
+  ntdreportingrr20_rural_data.ReportId as report_id,
+  ntdreportingrr20_rural_data.Item as item,
+  ntdreportingrr20_rural_data.Revenue as revenue,
+  ntdreportingrr20_rural_data.Type as type,
+  ntdreportingrr20_rural_data.CSSClass as css_class,
+  ntdreportingrr20_rural_data.OperationsExpended as operations_expended,
+  ntdreportingrr20_rural_data.CapitalExpended as capital_expended,
+  ntdreportingrr20_rural_data.Description as description,
+  ntdreportingrr20_rural_data.AnnualVehicleRevMiles as annual_vehicle_rev_miles,
+  ntdreportingrr20_rural_data.AnnualVehicleRevHours as annual_vehicle_rev_hours,
+  ntdreportingrr20_rural_data.AnnualUnlinkedPassTrips as annual_unlinked_pass_trips,
+  ntdreportingrr20_rural_data.AnnualVehicleMaxService as annual_vehicle_max_service,
+  ntdreportingrr20_rural_data.SponsoredServiceUPT as sponsored_service_upt,
+  ntdreportingrr20_rural_data.Quantity as quantity,
+  ntdreportingrr20_rural_data.LastModifiedDate as last_modified_date
+FROM {{ source('ntd_report_validation', 'all_ntdreports') }},
+ UNNEST(`ntdreportingrr20_rural_data`) as `ntdreportingrr20_rural_data`
diff --git a/warehouse/models/staging/ntd_validation/stg_ntd_2023_rr20_urban_tribal.sql b/warehouse/models/staging/ntd_validation/stg_ntd_2023_rr20_urban_tribal.sql
new file mode 100644
index 0000000000..02b61c3728
--- /dev/null
+++ b/warehouse/models/staging/ntd_validation/stg_ntd_2023_rr20_urban_tribal.sql
@@ -0,0 +1,16 @@
+SELECT
+  organization,
+  reportstatus as api_report_status,
+  TIMESTAMP_MILLIS(reportlastmodifieddate) as api_report_last_modified_date,
+  reportperiod as api_report_period,
+  ntdreportingrr20_urban_tribal_data.id as id,
+  ntdreportingrr20_urban_tribal_data.ReportId as report_id,
+  ntdreportingrr20_urban_tribal_data.ItemId as item_id,
+  ntdreportingrr20_urban_tribal_data.Item as item,
+  ntdreportingrr20_urban_tribal_data.OperationsExpended as operations_expended,
+  ntdreportingrr20_urban_tribal_data.CapitalExpended as capital_expended,
+  ntdreportingrr20_urban_tribal_data.Description as description,
+  ntdreportingrr20_urban_tribal_data.LastModifiedDate as last_modified_date
+FROM {{ source('ntd_report_validation', 'all_ntdreports') }},
+-- `cal-itp-data-infra-staging.external_blackcat.all_2023_ntdreports`
+ UNNEST(`ntdreportingrr20_urban_tribal_data`) as `ntdreportingrr20_urban_tribal_data`
diff --git a/warehouse/models/staging/ntd_validation/stg_ntd_subrecipients.sql b/warehouse/models/staging/ntd_validation/stg_ntd_subrecipients.sql
new file mode 100644
index 0000000000..c0282b37b4
--- /dev/null
+++ b/warehouse/models/staging/ntd_validation/stg_ntd_subrecipients.sql
@@ -0,0 +1,3 @@
+SELECT
+    Organization as organization
+FROM `cal-itp-data-infra.blackcat_raw.2023_organizations`
diff --git a/warehouse/models/staging/payments/littlepay/stg_littlepay__settlements.sql b/warehouse/models/staging/payments/littlepay/stg_littlepay__settlements.sql
index 4cbcca9d33..bfffa7474e 100644
--- a/warehouse/models/staging/payments/littlepay/stg_littlepay__settlements.sql
+++ b/warehouse/models/staging/payments/littlepay/stg_littlepay__settlements.sql
@@ -65,7 +65,16 @@ stg_littlepay__settlements AS (
         acquirer,
         refund_id,
         acquirer_response_rrn,
-        settlement_status,
+        -- manual backfill of ccjpa data for specific reporting needs
+        -- information on how to backfill comes from manual LP extracts provided by Rebel
+        CASE
+            WHEN _key IN ("4e0bf943884f81fd6e524f4e175139c0", "910e0fe9c6f78de301b69f35798ec613", "8df624e5048128983c9693faaa32f27d", "67e002b1be3f07b2c06b4335364d7fde", "f658f3201463764ceea23816e9c27a54")
+                THEN "REJECTED"
+            WHEN participant_id = "ccjpa"
+                AND littlepay_export_date < "2023-11-28"
+                THEN "SETTLED"
+            ELSE settlement_status
+        END AS settlement_status,
         request_created_timestamp_utc,
         response_created_timestamp_utc,
         _line_number,
diff --git a/warehouse/poetry.lock b/warehouse/poetry.lock
index 01a8cbdf7a..b8e0d69554 100644
--- a/warehouse/poetry.lock
+++ b/warehouse/poetry.lock
@@ -26,111 +26,99 @@ test = ["PyICU (>=2.4.2)", "coverage (>=3.7.1)", "cssselect (>=0.9.1)", "lxml (>
 
 [[package]]
 name = "aiohttp"
-version = "3.8.6"
+version = "3.9.0"
 description = "Async http client/server framework (asyncio)"
 optional = false
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+python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"