From bb6fe9b21ac31e6d24f13953c71077dacc6f1aad Mon Sep 17 00:00:00 2001 From: Willy Hardy Date: Wed, 10 Jul 2024 08:16:03 -0400 Subject: [PATCH 1/8] update the context set by the previous sections of the tutorial (#5765) ## What are you changing in this pull request and why? It is a little more clear to show the structure of the file that exists from previous parts of the tutorial. ## Checklist - [ x] Review the [Content style guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md) so my content adheres to these guidelines. - [ x] For [docs versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#about-versioning), review how to [version a whole page](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version) and [version a block of content](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-blocks-of-content). - [ x] Add a checklist item for anything that needs to happen before this PR is merged, such as "needs technical review" or "change base branch." --------- Co-authored-by: Mirna Wong <89008547+mirnawong1@users.noreply.github.com> --- website/docs/guides/mesh-qs.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/website/docs/guides/mesh-qs.md b/website/docs/guides/mesh-qs.md index 99b6c0f2e73..87fae1b58e0 100644 --- a/website/docs/guides/mesh-qs.md +++ b/website/docs/guides/mesh-qs.md @@ -437,16 +437,19 @@ How can you enhance resilience and add guardrails to this type of multi-project ### Set up model contracts As part of the Data Analytics team, you may want to ensure the `fct_orders` model is reliable for downstream users, like the Finance team. -1. Navigate to `models/core/core.yml` and under the `fct_orders` model, add a data contract to enforce reliability: +1. Navigate to `models/core/core.yml` and under the `fct_orders` model before the `columns:` section, add a data contract to enforce reliability: ```yaml models: - name: fct_orders access: public - description: "Customer and order details" + description: “Customer and order details” config: contract: enforced: true + columns: + - name: order_id + ..... ``` 2. Test what would happen if this contract were violated. In `models/core/fct_orders.sql`, comment out the `orders.status` column and click **Build** to try building the model. From 98ec5cb1278af24c7e36b343609e5d4e6a3e8da3 Mon Sep 17 00:00:00 2001 From: winnie <91998347+gwenwindflower@users.noreply.github.com> Date: Wed, 10 Jul 2024 09:17:07 -0500 Subject: [PATCH 2/8] Add 'Semantic Layer in pieces' blog post (#5759) ## What are you changing in this pull request and why? This PR adds a new blog post on iteratively building your dbt Semantic Layer! ## Checklist - [x] Review the [Content style guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md) so my content adheres to these guidelines. - [x] For [docs versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#about-versioning), review how to [version a whole page](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version) and [version a block of content](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-blocks-of-content). - [x] Add a checklist item for anything that needs to happen before this PR is merged, such as "needs technical review" or "change base branch." --------- Co-authored-by: Mirna Wong <89008547+mirnawong1@users.noreply.github.com> Co-authored-by: mirnawong1 --- ...-building-your-semantic-layer-in-pieces.md | 98 ++++++++++++++++++ .../elsa_meme.jpg | Bin 0 -> 66190 bytes .../rollup_dag.png | Bin 0 -> 147621 bytes 3 files changed, 98 insertions(+) create mode 100644 website/blog/2024-07-08-building-your-semantic-layer-in-pieces.md create mode 100644 website/static/img/blog/2024-07-09-semantic-layer-in-pieces/elsa_meme.jpg create mode 100644 website/static/img/blog/2024-07-09-semantic-layer-in-pieces/rollup_dag.png diff --git a/website/blog/2024-07-08-building-your-semantic-layer-in-pieces.md b/website/blog/2024-07-08-building-your-semantic-layer-in-pieces.md new file mode 100644 index 00000000000..53704131700 --- /dev/null +++ b/website/blog/2024-07-08-building-your-semantic-layer-in-pieces.md @@ -0,0 +1,98 @@ +--- +title: "How to build a Semantic Layer in pieces: step-by-step for busy analytics engineers" +description: A deep-dive into the steps you can take to start building your dbt Semantic Layer _today_, without doing a big migration. +slug: semantic-layer-in-pieces +authors: [gwen_windflower] +tags: [analytics craft] +hide_table_of_contents: false +date: 2024-07-10 +is_featured: true +--- + +The [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) is founded on the idea that data transformation should be both _flexible_, allowing for on-the-fly aggregations grouped and filtered by definable dimensions and _version-controlled and tested_. Like any other codebase, you should have confidence that your transformations express your organization’s business logic correctly. Historically, you had to choose between these options, but the dbt Semantic Layer brings them together. This has required new paradigms for _how_ you express your transformations though. + + + +Because of this, we’ve noticed when talking to dbt users that they _want_ to adopt the Semantic Layer, but feel daunted by the idea of migrating their transformations to this new paradigm. The good news is that you do _not_ need to make a huge one-time migration. + +We’re here to discuss another way: building a Semantic Layer in pieces. Our goal is to make sure you derive increased leverage and velocity from each step on your journey. If you’re eager to start building but have limited bandwidth (like most busy analytics engineers), this one is especially for you. + +## System of a noun: deciding what happens where + +When you’re using the dbt Semantic Layer, you want to _minimize_ _the modeling that exists outside of dbt_. Eliminate it completely if you can. Why? + +- It’s **duplicative, patchy, and confusing** as discussed above. +- It’s **less powerful**. +- You **can’t** **test** it. +- Depending on the tool, oftentimes you **can’t** **version control** it. + +What you want is a unified development flow that handles **normalized transformation in dbt models** and **dynamic denormalization in the dbt Semantic Layer** (meaning it dynamically combines and reshapes normalized data models into different formats whenever you need them). + +:::info +🏎️ **The Semantic Layer is a denormalization engine.** dbt transforms your data into clean, normalized marts. The dbt Semantic Layer is a denormalization engine that dynamically connects and molds these building blocks into the maximum amount of shapes available _dynamically_. +::: + +This enables a more **flexible consumption layer**, meaning downstream tools (like AI or dashboards) can sit as directly on top of Semantic Layer-generated artifacts and APIs as possible, and focus on what makes them shine instead of being burdened by basic dynamic modeling and aggregation tasks. Any tool-specific constructs should typically operate as close to **transparent pass-throughs** as you can make them, primarily serving to surface metrics and dimensions from the Semantic Layer in your downstream tool. There may be exceptions of course, but as a general guiding principle this gets you the most dynamic denormalization ability, and thus value, from your Semantic Layer code. + +So now we’ve established the system, let’s dig into the _plan_ for how we can get there iteratively. + +## The plan: towards iterative velocity + +1. **Identify a Data Product that is impactful** Find something that is in heavy use and high value, but fairly narrow scope. **Don’t start with a broad executive dashboard** that shows metrics from across the company because you’re looking to optimize for migrating the **smallest amount of modeling for the highest amount of impact** that you can. + + For example, a good starting place would be a dashboard focused on Customer Acquisition Cost (CAC) that relies on a narrow set of metrics and underlying tables that are nonetheless critical for your company. +2. **Catalog the models and their columns that service the Data Product**, both **in dbt _and_ the BI tool**, including rollups, metrics tables, and marts that support those. Pay special attention to aggregations as these will constitute _metrics_. You can reference [this example Google Sheet](https://docs.google.com/spreadsheets/d/1BR62C5jY6L5f5NvieMcA7OVldSFxu03Y07TG3waq0As/edit?usp=sharing) for one-way you might track this. +3. [**Melt the frozen rollups**](https://docs.getdbt.com/best-practices/how-we-build-our-metrics/semantic-layer-6-terminology) in your dbt project, as well as variations modeled in your BI tool, **into Semantic Layer code.** We’ll go much more in-depth on this process, and we encourage you to read more about this tactical terminology (frozen, rollup, etc) in the link — it will be used throughout this article! +4. **Create a parallel version of your data product that points to Semantic Layer artifacts, audit, and then publish.** Creating in parallel takes the pressure off, allowing you to fix any issues and publish gracefully. You’ll keep the existing Data Product as-is while swapping the clone to be supplied with data from the Semantic Layer. + + + +These steps constitute an **iterative piece** you will ship as you **progressively** move code into your Semantic Layer. As we dig into how to do this, we’ll discuss the **immediate value** this provides to your team and stakeholders. Broadly, it enables you to drastically increase [**iteration velocity**](https://www.linkedin.com/posts/rauchg_iteration-velocity-is-the-right-metric-to-activity-7087498430226313216-BVIP?utm_source=share&utm_medium=member_desktop). + +The process of **melting static, frozen tables** into more flexible, fluid, **dynamic Semantic Layer code** is not complex, but it’s helpful to dig into the specific steps in the process. In the next section, we’ll dive into what this looks like in practice so you have a solid understanding of the "what’s required". + +This is the most **technical, detailed, and specific section of this article**, so make sure to bookmark it and **reference it** as often as you can until the process becomes as intuitive as regular modeling in dbt! + +## Migrating a chunk: step-by-step + +### 1. Identify target + +1. **Identify a relatively normalized mart that is powering rollups in dbt**. If you do your rollups in your BI tool, start there. But we recommend starting with the frozen tables in dbt _first_ and moving through the flow of the DAG progressively, bringing logic in your BI tool into play last. This is because we want to iteratively break up these frozen concepts in such a way that we benefit from earlier parts of the chain being migrated already. Think "moving left-to-right in a big DAG" that spans all your tools. + - ✅ `orders`, `customers` — these are basic concepts powering your business, so should be marts models materialized via dbt. + - ❌ `active_accounts_per_week` — this is built on top of the above, and something we want to generate dynamically in the dbt Semantic Layer. + - Put another way: `customers` and `orders` are **normalized building blocks**, `active_accounts_per_week` is a **rollup** and we always want to _migrate those to the Semantic Layer_. + + +### 2. Catalog the inputs + +1. Identify **normalized columns** and **ignore any aggregation columns** for now. For example, `order_id`, `ordered_at`, `customer_id`, `order_total` are fields we want to put in our semantic model, a window function that sums `customer_cac` _statically_ in the dbt model is _not_ a field we want in our semantic model because we want to _dynamically_ codify that calculation as a metric in the Semantic Layer. + 1. If you find in the next step that you can’t express a certain calculation in the Semantic Layer yet, use dbt to model it**.** This is the beauty of having your Semantic Layer code integrated in your dbt codebase, it’s easy to manage the push and pull of the line between the Transformation and Semantic Layers because you’re managing **a cohesive set of code and tooling.** + +### 3. Write Semantic Layer code + +1. **Start with the semantic model** going through column by column and putting all identified columns from Step 2 into the 3 semantic buckets: + 1. [**Entities**](/docs/build/entities) — these are the spine of your semantic concepts or objects, you can think of them as roughly correlating to IDs or keys that form the grain. + 2. [**Dimensions**](/docs/build/dimensions) — these are ways of grouping and bucketing these objects or concepts, such as time and categories. + 3. [**Measures**](/docs/build/measures) — these are numeric values that you want to aggregate such as an order total or number of times a user clicked an ad. +2. **Create metrics for the aggregation columns** we didn’t encode into the semantic model. +3. Now, **identify a rollup you want to melt**. Refer to the [earlier example](#1-identify-target) to help distinguish these types of models. +4. **Repeat these steps for any** **other concepts** that you need to create that rollup e.g. `active_accounts_per_week` may need **both `customers` and `orders`.** +5. **Create metrics for the aggregation columns present in the rollup**. If your rollup references multiple models, put metrics in the YAML file that is most closely related to the grain or key aggregation of the table. For example, `active_accounts_per_week` is aggregated at a weekly time grain, but the key metric counts customer accounts, so we’d want to put that metric in the `customers.yml` or `sem_customers.yml` file (depending on [the naming system](/best-practices/how-we-build-our-metrics/semantic-layer-7-semantic-structure) you prefer). If it also contained a metric aggregating total orders in a given week, we’d put that metric into `orders.yml` or `sem_orders.yml`. +6. **Create [saved queries with exports](/docs/build/saved-queries)** configured to materialize your new Semantic Layer-based artifacts into the warehouse in parallel with the frozen rollup. This will allow us to shift consumption tools and audit results. + +### 4. Connect external tools in parallel + +1. Now, **shift your external analysis tool to point at the Semantic Layer exports instead of the rollup**. Remember, we only want to shift the pointers for the rollup that we’ve migrated, everything else should stay pointing to frozen rollups. We’re migrating iteratively in pieces! + 1. If your downstream tools have an integration with the Semantic Layer, you’ll want to set that up as well. This will allow not only [declarative caching](/docs/use-dbt-semantic-layer/sl-cache#declarative-caching) of common query patterns with exports but also easy, totally dynamic on-the-fly queries. +2. Once you’ve replicated the previous state of things, with the Semantic Layer providing the data instead of frozen rollups, now you’re ready to **shift the transformations happening in your BI tool into the Semantic Layer**, following the same process. +3. Finally, to **feel the new speed and power you’ve unlocked**, ask a stakeholder for a dimension or metric that’s on their wishlist for the data product you’re working with. Then, bask in the glory of amazing them when you ship it an hour later! + +:::tip +💁🏻‍♀️ If your BI tool allows it, make sure to do the BI-related steps above **in a development environment**. If it doesn’t have these capabilities, stick with duplicating the data product you’re re-building and perform this there so you can swap it later after you’ve tested it thoroughly. +::: + +## Deep impact + +The first time you turn around a newly sliced, diced, filtered, and rolled up metric table for a stakeholder in under an hour instead of a week, not only you, but the stakeholder will immediately feel the value and power of the Semantic Layer. + +dbt Labs’ mission is to create and disseminate organizational knowledge. This process, and building a Semantic Layer generally, is about encoding organizational knowledge in such a way that it creates and disseminates _leverage_. Enabled by this process, you can start building your Semantic Layer _today_, without waiting for the magical capacity for a giant overhaul to materialize. 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z!*9~!srT!D1gemkT!=TDatalC$Q_$#8%X}UHsaFtGum8FmY9{SeULkuT&3v!~x19)at;QY?-D{=sFA zL*Y%6WKNQbYLj`x{a4g|q?c{6mvKvGBUK|W@0;Lwxo;z|e_kT1{DP1Hi*(9ri$^~5 zyt&ro&>6MGh8SP`+sp4V4^tL&mIDD6D7epx)6 z03bb46ZdWEY26N}BA5f}-w6)fGx+Sjr6 Npl5Oncl5%|{{ysGJevRj literal 0 HcmV?d00001 From 9fc91a0b3808b7cca4c1a750e7e4968702e7e316 Mon Sep 17 00:00:00 2001 From: SoufianeK <37109813+soufianekhoudmi@users.noreply.github.com> Date: Wed, 10 Jul 2024 18:02:31 +0200 Subject: [PATCH 3/8] Add dbt Make Open Data project example (#5758) ## What are you changing in this pull request and why? Add Make Open Data entry to dbt project example [ ] Review the [Content style guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md) so my content adheres to these guidelines. [ ] For [docs versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#about-versioning), review how to [version a whole page](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version) and [version a block of content](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-blocks-of-content). --------- Co-authored-by: Mirna Wong <89008547+mirnawong1@users.noreply.github.com> --- website/docs/faqs/Project/example-projects.md | 1 + 1 file changed, 1 insertion(+) diff --git a/website/docs/faqs/Project/example-projects.md b/website/docs/faqs/Project/example-projects.md index 2dfffed02f6..b2ed1cc92ab 100644 --- a/website/docs/faqs/Project/example-projects.md +++ b/website/docs/faqs/Project/example-projects.md @@ -13,5 +13,6 @@ Yes! * **GitLab:** Gitlab's internal dbt project is open source and is a great example of how to use dbt at scale ([source code](https://gitlab.com/gitlab-data/analytics/-/tree/master/transform/snowflake-dbt)) * **dummy-dbt:** A containerized dbt project that populates the Sakila database in Postgres and populates dbt seeds, models, snapshots, and tests. The project can be used for testing and experimentation purposes ([source code](https://github.com/gmyrianthous/dbt-dummy)) * **Google Analytics 4:** A demonstration project that transforms the Google Analytics 4 BigQuery exports to various models ([source code](https://github.com/stacktonic-com/stacktonic-dbt-example-project), [docs](https://stacktonic.com/article/google-analytics-big-query-and-dbt-a-dbt-example-project)) +* **Make Open Data:** A production-grade ELT with tests, documentation, and CI/CD (GHA) about French open data (housing, demography, geography, etc). It can be used to learn with voluminous and ambiguous data. Contributions are welcome ([source code](https://github.com/make-open-data/make-open-data), [docs](https://make-open-data.fr/)) If you have an example project to add to this list, suggest an edit by clicking **Edit this page** below. From 6b6e1a10ba18fffcc2b75b3cb92412602439d52b Mon Sep 17 00:00:00 2001 From: winnie <91998347+gwenwindflower@users.noreply.github.com> Date: Wed, 10 Jul 2024 13:34:18 -0500 Subject: [PATCH 4/8] fix(metrics overview): clean up typo in example (#5723) This example looks like it contains invalid YAML, I believe the correct YAML is this. Will confirm tomorrow, don't merge this until confirmed please, thank you! Co-authored-by: Mirna Wong <89008547+mirnawong1@users.noreply.github.com> --- website/docs/docs/build/metrics-overview.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md index 3108f30c374..16effcccd23 100644 --- a/website/docs/docs/build/metrics-overview.md +++ b/website/docs/docs/build/metrics-overview.md @@ -128,10 +128,7 @@ metrics: name: active_users fill_nulls_with: 0 join_to_timespine: true - measure: - name: distinct_users window: 7 days - ``` ### Derived metrics From 4dae42754a677a816b7906348f987fd44a39d4ec Mon Sep 17 00:00:00 2001 From: Mirna Wong <89008547+mirnawong1@users.noreply.github.com> Date: Wed, 10 Jul 2024 19:39:55 +0100 Subject: [PATCH 5/8] update scd docs per feedback (#5754) Opened this pr to address the feedback from issue #5395. It also adds info regarding validity_params and how it relates to the scd table columns: valid_to/valid_from. [ ] Outstanding question wrt metric_time [ ] PM review needd Resolves #5395 --- website/docs/docs/build/dimensions.md | 78 ++++++++++++++++++++------- 1 file changed, 58 insertions(+), 20 deletions(-) diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md index 6064e67702a..d74bc773ea9 100644 --- a/website/docs/docs/build/dimensions.md +++ b/website/docs/docs/build/dimensions.md @@ -45,8 +45,8 @@ semantic_models: description: A record for every transaction that takes place. Carts are considered multiple transactions for each SKU. model: {{ ref("fact_transactions") }} defaults: - agg_time_dimension: metric_time -# --- entities --- + agg_time_dimension: order_date +# --- entities --- entities: ... # --- measures --- @@ -86,6 +86,10 @@ This section further explains the dimension definitions, along with examples. Di - [Categorical](#categorical) - [Time](#time) - [SCD Type II](#scd-type-ii) + - [Basic structure](#basic-structure) + - [Semantic model parameters and keys](#semantic-model-parameters-and-keys) + - [Implementation](#implementation) + - [SCD examples](#scd-examples) ## Categorical @@ -212,31 +216,62 @@ measures: Currently, there are limitations in supporting SCDs. ::: -MetricFlow supports joins against dimensions values in a semantic model built on top of a slowly changing dimension (SCD) Type II table. This is useful when you need a particular metric sliced by a group that changes over time, such as the historical trends of sales by a customer's country. +MetricFlow supports joins against dimensions values in a semantic model built on top of a slowly changing dimension (SCD) Type II table. This is useful when you need a particular metric sliced by a group that changes over time, such as the historical trends of sales by a customer's country. #### Basic structure -SCD Type II are groups that change values at a coarser time granularity. This results in a range of valid rows with different dimensions values for a given metric or measure. MetricFlow associates the metric with the first (minimum) available dimension value within a coarser time window, such as month. By default, MetricFlow uses the group that is valid at the beginning of the time granularity. +SCD Type II are groups that change values at a coarser time granularity. SCD Type II tables typically have two time columns that indicate the validity period of a dimension: `valid_from` (or `tier_start`) and `valid_to` (or `tier_end`). This creates a range of valid rows with different dimension values for a metric or measure. + +MetricFlow associates the metric with the earliest available dimension value within a coarser time window, such as a month. By default, it uses the group valid at the start of this time granularity. -The following basic structure of an SCD Type II data platform table is supported: +MetricFlow supports the following basic structure of an SCD Type II data platform table: | entity_key | dimensions_1 | dimensions_2 | ... | dimensions_x | valid_from | valid_to | |------------|-------------|-------------|-----|-------------|------------|----------| -* `entity_key` (required): An entity_key (or some sort of identifier) must be present. -* `valid_from` (required): A timestamp indicating the start of a changing dimensions value must be present -* `valid_to` (required): A timestamp indicating the end of a changing dimensions value must be present +* `entity_key` (required): A unique identifier for each row in the table, such as a primary key or another unique identifier specific to the entity. +* `valid_from` (required): Start date timestamp for when the dimension is valid. Use `validity_params: is_start: True` in the semantic model to specify this. +* `valid_to` (required): End date timestamp for when the dimension is valid. Use `validity_params: is_end: True` in the semantic model to specify this. + +#### Semantic model parameters and keys +When configuring an SCD Type II table in a semantic model, use `validity_params` to specify the start (`valid_from`) and end (`valid_to`) of the validity window for each dimension. -#### SCD tables and keys +- `validity_params`: Parameters that define the validity window. + - `is_start: True`: Indicates the start of the validity period. Displayed as `valid_from` in the SCD table. + - `is_end: True`: Indicates the end of the validity period. Displayed as `valid_to` in the SCD table. + +Here’s an example configuration: + +```yaml +- name: tier_start # The name of the dimension. + type: time # The type of dimension (such as time) + label: "Start date of tier" # A readable label for the dimension + expr: start_date # Expression or column name the the dimension represents + type_params: # Additional parameters for the dimension type + time_granularity: day # Specifies the granularity of the time dimension (such as day) + validity_params: # Defines the validity window + is_start: True # Indicates the start of the validity period. +- name: tier_end + type: time + label: "End date of tier" + expr: end_date + type_params: + time_granularity: day + validity_params: + is_end: True # Indicates the end of the validity period. +``` -SCD Type II tables have a specific dimension with a start and end date. To join tables, set the additional [entity `type`](/docs/build/entities#entity-types) parameter to the `natural` key. Using a `natural` key as an [entity `type`](/docs/build/entities#entity-types) means you also don't usually need a `primary` key. In most instances, SCD tables don't have a logically usable `primary` key because `natural` keys map to multiple rows. +SCD Type II tables have a specific dimension with a start and end date. To join tables: +- Set the additional [entity `type`](/docs/build/entities#entity-types) parameter to the `natural` key. +- Use a `natural` key as an [entity `type`](/docs/build/entities#entity-types), which means you don't need a `primary` key. +- In most instances, SCD tables don't have a logically usable `primary` key because `natural` keys map to multiple rows. #### Implementation Here are some guidelines to follow when implementing SCD Type II tables: -- The SCD semantic model must have `valid_to` and `valid_from` time dimensions, which are logical constructs. -- The `valid_from` and `valid_to` properties must be specified exactly once per SCD semantic model. +- The SCD table must have `valid_to` and `valid_from` time dimensions, which are logical constructs. +- The `valid_from` and `valid_to` properties must be specified exactly once per SCD table configuration. - The `valid_from` and `valid_to` properties shouldn't be used or specified on the same time dimension. - The `valid_from` and `valid_to` time dimensions must cover a non-overlapping period where one row matches each natural key value (meaning they must not overlap and should be distinct). - We recommend defining the underlying dbt model with [dbt snapshots](/docs/build/snapshots). This supports the SCD Type II table layout and ensures that the table is updated with the latest data. @@ -254,11 +289,11 @@ on group by 1, 2 ``` -#### SCD example +#### SCD examples - +The following are examples of how to use SCD Type II tables in a semantic model: - + This example shows how to create slowly changing dimensions (SCD) using a semantic model. The SCD table contains information about salespersons' tier and the time length of that tier. Suppose you have the underlying SCD table: @@ -270,7 +305,11 @@ This example shows how to create slowly changing dimensions (SCD) using a semant | 333 | 2 | 2020-08-19 | 2021-10-22| | 333 | 3 | 2021-10-22 | 2048-01-01| -The `validity_params` include two important arguments — `is_start` and `is_end`. These specify the columns in the SCD table that mark the start and end dates (or timestamps) for each tier or dimension. Additionally, the entity is tagged as `natural` to differentiate it from a `primary` entity. In a `primary` entity, each entity value has one row. In contrast, a `natural` entity has one row for each combination of entity value and its validity period. +As mentioned earlier, the `validity_params` include two important arguments that specify the columns in the SCD table that mark the start and end dates (or timestamps) for each tier or dimension: +- `is_start` +- `is_end` + +Additionally, the entity is tagged as `natural` to differentiate it from a `primary` entity. In a `primary` entity, each entity value has one row. In contrast, a `natural` entity has one row for each combination of entity value and its validity period. ```yaml semantic_models: @@ -361,9 +400,9 @@ You can now access the metrics in the `transactions` semantic model organized by In the sales tier example, For instance, if a salesperson was Tier 1 from 2022-03-01 to 2022-03-12, and gets promoted to Tier 2 from 2022-03-12 onwards, all transactions from March would be categorized under Tier 1 since the dimensions value of Tier 1 comes earlier (and is the default starting point), even though the salesperson was promoted to Tier 2 on 2022-03-12. - + - + This example shows how to create slowly changing dimensions (SCD) using a semantic model. The SCD table contains information about salespersons' tier and the time length of that tier. Suppose you have the underlying SCD table: @@ -388,5 +427,4 @@ mf query --metrics transactions --group-by metric_time__month,sales_person__tier ``` - - + From 754375a20f395ccabc0a9fdd30a0eaa05b6f8c21 Mon Sep 17 00:00:00 2001 From: Mirna Wong <89008547+mirnawong1@users.noreply.github.com> Date: Thu, 11 Jul 2024 12:13:16 +0100 Subject: [PATCH 6/8] Update project-recommendations.md (#5738) add info about package being base version and doesn't import customization. raised here; https://dbt-labs.slack.com/archives/C04HJR9B69H/p1720019114070189?thread_ts=1719937667.452869&cid=C04HJR9B69H --- .../docs/docs/collaborate/project-recommendations.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/website/docs/docs/collaborate/project-recommendations.md b/website/docs/docs/collaborate/project-recommendations.md index 7a4ca4bb961..369076d9d97 100644 --- a/website/docs/docs/collaborate/project-recommendations.md +++ b/website/docs/docs/collaborate/project-recommendations.md @@ -4,12 +4,13 @@ sidebar_label: "Project recommendations" description: "dbt Explorer provides recommendations that you can take to improve the quality of your dbt project." --- -dbt Explorer provides recommendations about your project from the `dbt_project_evaluator` [package](https://hub.getdbt.com/dbt-labs/dbt_project_evaluator/latest/) using metadata from the Discovery API. +dbt Explorer provides recommendations about your project from the `dbt_project_evaluator` [package](https://hub.getdbt.com/dbt-labs/dbt_project_evaluator/latest/) using metadata from the [Discovery API](/docs/dbt-cloud-apis/discovery-api). -Explorer also offers a global view, showing all the recommendations across the project for easy sorting and summarizing. - -These recommendations provide insight into how you can build a more well documented, well tested, and well built project, leading to less confusion and more trust. +- Explorer also offers a global view, showing all the recommendations across the project for easy sorting and summarizing. +- These recommendations provide insight into how you can create a better-documented, better-tested, and better-built dbt project, creating more trust and less confusion. +- For a seamless and consistent experience, recommendations use `dbt_project_evaluator`'s pre-defined settings and don't import customizations applied to your package or project. +## Recommendations page The Recommendations overview page includes two top-level metrics measuring the test and documentation coverage of the models in your project. - **Model test coverage** — The percent of models in your project (models not from a package or imported via dbt Mesh) with at least one dbt test configured on them. @@ -18,6 +19,7 @@ The Recommendations overview page includes two top-level metrics measuring the t ## List of rules +The following table lists the rules currently defined in the `dbt_project_evaluator` [package](https://hub.getdbt.com/dbt-labs/dbt_project_evaluator/latest/). | Category | Name | Description | Package Docs Link | | --- | --- | --- | --- | @@ -37,7 +39,7 @@ The Recommendations overview page includes two top-level metrics measuring the t ## The Recommendations tab -Models, sources and exposures each also have a Recommendations tab on their resource details page, with the specific recommendations that correspond to that resource: +Models, sources, and exposures each also have a **Recommendations** tab on their resource details page, with the specific recommendations that correspond to that resource: From f347cbab8ce621bc249c802731387bdc812c1575 Mon Sep 17 00:00:00 2001 From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com> Date: Thu, 11 Jul 2024 17:06:01 -0600 Subject: [PATCH 7/8] Link all all migration guides instead of hard-pinning to v1.4 migration guide (#5775) [Preview](https://docs-getdbt-com-git-dbeatty10-patch-1-dbt-labs.vercel.app/docs/core/installation-overview#upgrading-dbt-core) ## What are you changing in this pull request and why? Two things: 1. Update the link to the migration guide(s) 1. Improve the sentence it appears within (remove the comma and add a period at the end) ## Checklist - [x] Review the [Content style guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md) so my content adheres to these guidelines. --- website/docs/docs/core/installation-overview.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/docs/core/installation-overview.md b/website/docs/docs/core/installation-overview.md index fac93475199..7ae61be9104 100644 --- a/website/docs/docs/core/installation-overview.md +++ b/website/docs/docs/core/installation-overview.md @@ -32,7 +32,7 @@ You can install dbt Core on the command line by using one of these methods: ## Upgrading dbt Core -dbt provides a number of resources for understanding [general best practices](/blog/upgrade-dbt-without-fear) while upgrading your dbt project as well as detailed [migration guides](/docs/dbt-versions/core-upgrade/upgrading-to-v1.4) highlighting the changes required for each minor and major release, and [core versions](/docs/dbt-versions/core) +dbt provides a number of resources for understanding [general best practices](/blog/upgrade-dbt-without-fear) while upgrading your dbt project as well as detailed [migration guides](/docs/dbt-versions/core-upgrade) highlighting the changes required for each [minor and major release](/docs/dbt-versions/core). - [Upgrade `pip`](/docs/core/pip-install#change-dbt-core-versions) From 946199556e79a9106d4ec06452f6de081cf28193 Mon Sep 17 00:00:00 2001 From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com> Date: Fri, 12 Jul 2024 02:27:36 -0600 Subject: [PATCH 8/8] Add section headers to separate the different sections (#5776) [Preview](https://docs-getdbt-com-git-dbeatty10-patch-1-dbt-labs.vercel.app/reference/resource-configs/contract) ## What are you changing in this pull request and why? The [`contracts`](https://docs.getdbt.com/reference/resource-configs/contract#example) page includes some important information as it relates to size, precision, and scale when enforcing contracts, but it's hard to pick out visually. I want to be send hyperlinks to folks that points to just the right section. For example, in this issue: https://github.com/dbt-labs/dbt-core/issues/10430. ## Checklist - [x] Review the [Content style guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md) so my content adheres to these guidelines. --- website/docs/reference/resource-configs/contract.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/website/docs/reference/resource-configs/contract.md b/website/docs/reference/resource-configs/contract.md index bf543f0eec9..5904a6cc69d 100644 --- a/website/docs/reference/resource-configs/contract.md +++ b/website/docs/reference/resource-configs/contract.md @@ -14,6 +14,8 @@ When the `contract` configuration is enforced, dbt will ensure that your model's This is to ensure that the people querying your model downstream—both inside and outside dbt—have a predictable and consistent set of columns to use in their analyses. Even a subtle change in data type, such as from `boolean` (`true`/`false`) to `integer` (`0`/`1`), could cause queries to fail in surprising ways. +## Data type aliasing + The `data_type` defined in your YAML file must match a data type your data platform recognizes. dbt does not do any type aliasing itself. If your data platform recognizes both `int` and `integer` as corresponding to the same type, then they will return a match. @@ -42,11 +44,13 @@ models: +## Size, precision, and scale + When dbt compares data types, it will not compare granular details such as size, precision, or scale. We don't think you should sweat the difference between `varchar(256)` and `varchar(257)`, because it doesn't really affect the experience of downstream queriers. You can accomplish a more-precise assertion by [writing or using a custom test](/best-practices/writing-custom-generic-tests). Note that you need to specify a varchar size or numeric scale, otherwise dbt relies on default values. For example, if a `numeric` type defaults to a precision of 38 and a scale of 0, then the numeric column stores 0 digits to the right of the decimal (it only stores whole numbers), which might cause it to fail contract enforcement. To avoid this implicit coercion, specify your `data_type` with a nonzero scale, like `numeric(38, 6)`. dbt Core 1.7 and higher provides a warning if you don't specify precision and scale when providing a numeric data type. -## Example +### Example