page link fixes

docs/docs-beta/docs/getting-started/quickstart.md

@@ -153,5 +153,5 @@ id,name,age,city,age_group
 
 Congratulations! You've just built and run your first pipeline with Dagster. Next, you can:
 
-- Continue with the [ETL pipeline tutorial](/tutorial/tutorial-etl) to learn how to build a more complex ETL pipeline
+- Continue with the [ETL pipeline tutorial](/tutorial/etl-tutorial/etl-tutorial-introduction) to learn how to build a more complex ETL pipeline
 - Learn how to [Think in assets](/guides/build/assets-concepts/index.md)

docs/docs-beta/docs/tutorial/etl-tutorial/01-etl-tutorial-introduction.md

@@ -130,9 +130,7 @@ To make sure Dagster and its dependencies were installed correctly, navigate to
 
 followed by a bash code snippet for `dagster dev`
 
-[screenshot of ui]
-
 
 ## Next steps
 
-- Continue this tutorial by [creating and materializing assets](/tutorial/02-create-and-materialize-assets)
+- Continue this tutorial by [creating and materializing assets](/tutorial/etl-tutorial/02-create-and-materialize-assets)

docs/docs-beta/docs/tutorial/etl-tutorial/02-create-and-materialize-assets.md

@@ -35,7 +35,7 @@ Open the `definitions.py` file in the `etl_tutorial` directory and copy the foll
 
 ## 2. Define the DuckDB resource
 
-In Dagster, [Resources](/api/resources) are the external services, tools, and storage backends you need to do your job. For the storage backend in this project, we'll use [DuckDB](https://duckdb.org/), a fast, in-process SQL database that runs inside your application. We'll define it once in the definitions object, making it available to all assets and objects that need it.
+In Dagster, [Resources API docs](/todo) are the external services, tools, and storage backends you need to do your job. For the storage backend in this project, we'll use [DuckDB](https://duckdb.org/), a fast, in-process SQL database that runs inside your application. We'll define it once in the definitions object, making it available to all assets and objects that need it.
 
   ```python
   defs = dg.Definitions(
@@ -46,7 +46,7 @@ In Dagster, [Resources](/api/resources) are the external services, tools, and st
 
 ## 3. Create assets
 
-Software defined [assets](/api/assets) are the main building blocks in Dagster. An asset is composed of three components:
+Software defined [assets API docs](/todo) are the main building blocks in Dagster. An asset is composed of three components:
 1. Asset key or unique identifier.
 2. An op which is a function that is invoked to produce the asset.
 3. Upstream dependencies that the asset depends on. 
@@ -105,4 +105,4 @@ To materialize your assets:
 
 ## Next steps
 
-- Continue this tutorial with your with your [asset dependencies](/tutorial/03-creating-a-downstream-asset)
+- Continue this tutorial with your with your [asset dependencies](/tutorial/etl-tutorial/03-creating-a-downstream-asset)

docs/docs-beta/docs/tutorial/etl-tutorial/03-create-and-materialize-a-downstream-asset.md

@@ -7,7 +7,7 @@ last_update:
 
 # Asset Dependencies
 
-Now that we have the raw data loaded into DuckDB, we need to create a [downstream asset](guides/build/asset-concepts/asset-dependencies) that combines the upstream assets together. In this step, you will:
+Now that we have the raw data loaded into DuckDB, we need to create a [downstream asset](/guides/build/assets-concepts/asset-dependencies) that combines the upstream assets together. In this step, you will:
 
 - Create a downstream asset
 - Materialize that asset
@@ -43,4 +43,4 @@ Your Definitions object should now look like this:
 
 ## Next steps
 
-- Continue this tutorial with [create and materialize a partitioned asset](/tutorial/05-ensuring-data-quality-with-asset-checks)
+- Continue this tutorial with [create and materialize a partitioned asset](/tutorial/etl-tutorial/05-ensuring-data-quality-with-asset-checks)

docs/docs-beta/docs/tutorial/etl-tutorial/04-ensuring-data-quality-with-asset-checks.md

@@ -9,7 +9,7 @@ last_update:
 
 Data Quality is critical in data pipelines. Much like in a factory producing cars, inspecting parts after they complete certain steps ensures that defects are caught before the car is completely assembled. 
 
-In Dagster, you define [asset checks](guides/build/test/asset-checks) in a similar way that you would define an Asset. In this step you will:
+In Dagster, you define [asset checks](/guides/test/asset-checks) in a similar way that you would define an Asset. In this step you will:
 
 - Define an asset check
 - Execute that asset check in the UI
@@ -50,4 +50,4 @@ Asset checks will run when an asset is materialized, but asset checks can also b
 
 ## Next steps
 
-- Continue this tutorial with [Asset Checks](/tutorial/04-ensuring-data-quality-with-asset-checks)
+- Continue this tutorial with [Asset Checks](/tutorial/etl-tutorial/04-ensuring-data-quality-with-asset-checks)

docs/docs-beta/docs/tutorial/etl-tutorial/05-create-and-materialize-partitioned-asset.md

@@ -8,7 +8,7 @@ last_update:
 
 # Partitions
 
-[Partitions](guides/partitioning) are a core abstraction in Dagster, they are how you manage large datasets, process incremental updates, and improve pipeline performance. In Dagster you can partition assets the following ways:
+[Partitions](/guides/create-a-pipeline/partitioning) are a core abstraction in Dagster, they are how you manage large datasets, process incremental updates, and improve pipeline performance. In Dagster you can partition assets the following ways:
 
 1. Time-based: Split data by time periods (e.g., daily, monthly)
 2. Category-based: Divide by known categories (e.g., country, product type)
@@ -177,4 +177,4 @@ To materialize these assets :
 
 ## Next Steps
 
-Now that we have the main assets in our ETL pipeline, its time to add [automation to our pipeline](tutorial/06-automating-your-pipeline)
+Now that we have the main assets in our ETL pipeline, its time to add [automation to our pipeline](/tutorial/etl-tutorial/06-automating-your-pipeline)

docs/docs-beta/docs/tutorial/etl-tutorial/06-automating-your-pipeline.md

@@ -7,7 +7,7 @@ last_update:
 
 # Automation
 
-There are several ways to automate pipelines and assets [in Dagster](guides/automation). 
+There are several ways to automate pipelines and assets [in Dagster](/guides/automate). 
 
 In this step you will:
 
@@ -16,23 +16,23 @@ In this step you will:
 
 ## 1. Automating asset materialization 
 
-Ideally, the reporting assets created in the last step should refresh whenever the upstream data is updated. This can be done simply using [declarative automation](guides/declarative-automation) and adding an automation condition to the asset definition.
+Ideally, the reporting assets created in the last step should refresh whenever the upstream data is updated. This can be done simply using [declarative automation](/guides/automate/declarative-automation) and adding an automation condition to the asset definition.
 
 Update the `monthly_sales_performance` asset to have the automation condition in the decorator:
 
 <CodeExample filePath="guides/tutorials/etl_tutorial/etl_tutorial/definitions.py" language="python" lineStart="155" lineEnd="209"/>
 
 Do the same thing for `product_performance`:
 
-<CodeExample filePath="guides/tutorials/etl_tutorial/etl_tutorial/definitions.py" language="python" lineStart="217" lineEnd="267"/>
+<CodeExample filePath="guides/tutorials/etl_tutorial/etl_tutorial/definitions.py" language="python" lineStart="216" lineEnd="267"/>
 
 ## 2. Scheduled Jobs
 
 CRON based schedules are common in data orchestration. For our pipeline, assume updated csv's get dropped into a file location every week at a specified time by an external process. We want to have a job that runs the pipeline and materialize the asset. Since we already defined the performance assets to materialize using the eager condition, When the upstream data is updated the entire pipeline will refresh. 
 
 Copy the following code underneath the `product performance` asset:
 
-<CodeExample filePath="guides/tutorials/etl_tutorial/etl_tutorial/definitions.py" language="python" lineStart="267" lineEnd="273"/>
+<CodeExample filePath="guides/tutorials/etl_tutorial/etl_tutorial/definitions.py" language="python" lineStart="268" lineEnd="273"/>
 
 ## 3. Running the entire pipeline
 
@@ -54,4 +54,4 @@ Additionally if you navigate to the runs tab you will see that materializations
 
 ## Next steps
 
-- Continue this tutorial with adding a [sensor based asset](/tutorial/07-creating-a-sensor-asset)
+- Continue this tutorial with adding a [sensor based asset](/tutorial/etl-tutorial/07-creating-a-sensor-asset)

docs/docs-beta/docs/tutorial/etl-tutorial/07-creating-a-sensor-asset.md

@@ -5,9 +5,7 @@ last_update:
   author: Alex Noonan
 ---
 
-# Sensors 
-
-[Sensors](guides/sensors) in Dagster are a powerful tool for automating workflows based on external events or conditions. They allow you to trigger jobs when specific criteria are met, making them essential for event-driven automation. 
+[Sensors](/guides/automate/sensors) in Dagster are a powerful tool for automating workflows based on external events or conditions. They allow you to trigger jobs when specific criteria are met, making them essential for event-driven automation. 
 
 Event driven automations to support situations where jobs occur at irregular cadences or in rapid succession.  is the building block in Dagster you can use to support this. 
 
@@ -23,7 +21,7 @@ In this step you will:
 
 ## 1. Event Driven Asset
 
-For our pipeline, we want to model a situation where an executive wants a pivot table report of sales results by department and product. They want that processed in real time from their request and it isnt a high priority to build the reporting to have this available and refreshing. 
+For our pipeline, we want to model a situation where an executive wants a pivot table report of sales results by department and product. They want that processed in real time from their request and it isn't a high priority to build the reporting to have this available and refreshing. 
 
 For this asset we need to define the structure of the request that it is expecting in the materialization context. 
 
@@ -70,4 +68,4 @@ sensors include the following elements:
 
 Now that we have our complete project, the next step is to refactor the project into more a more manageable structure so we can add to it as needed. 
 
-Finish the tutorial with [refactoring the project](tutorial/refactoring-the-project)
+Finish the tutorial with [refactoring the project](/tutorial/etl-tutorial/08-refactoring-the-project)

docs/docs-beta/docs/tutorial/etl-tutorial/08-refactoring-the-project.md

@@ -5,8 +5,6 @@ last_update:
   author: Alex Noonan
 ---
 
-# Refactoring code
-
 Many engineers generally leave something alone once its working as expected. But the first time you do something is rarely the best implementation of a use case and all projects benefit from incremental improvements.
 
 ## Splitting up project structure