[du] consistency issues

docs/dagster-university/next-env.d.ts

@@ -2,4 +2,4 @@
 /// <reference types="next/image-types/global" />
 
 // NOTE: This file should not be edited
-// see https://nextjs.org/docs/basic-features/typescript for more information.
+// see https://nextjs.org/docs/pages/building-your-application/configuring/typescript for more information.

docs/dagster-university/pages/dagster-essentials/lesson-2/requirements-and-installation.md

@@ -9,11 +9,12 @@ lesson: '2'
 To install Dagster, you’ll need:
 
 - **To install Python**. Dagster supports Python 3.9 through 3.12.
-- **A package manager like pip or poetry**. If you need to install a package manager, refer to the following installation guides:
+- **A package manager like pip, Poetry, or uv**. If you need to install a package manager, refer to the following installation guides:
   - [pip](https://pip.pypa.io/en/stable/installation/)
   - [Poetry](https://python-poetry.org/docs/)
+  - [uv](https://docs.astral.sh/uv/getting-started/installation/)
 
-To check that Python and the pip or Poetry package manager are already installed in your environment, run:
+To check that Python and the package manager are already installed in your environment, run:
 
 ```shell
 python --version

docs/dagster-university/pages/dagster-essentials/lesson-3/whats-an-asset.md

@@ -10,8 +10,8 @@ An asset is an object in persistent storage that captures some understanding of
 
 - **A database table or view**, such as those in a Google BigQuery data warehouse
 - **A file**, such as a file in your local machine or blob storage like Amazon S3
-- **A machine learning model**
-- **An asset from an integration,** like a dbt model or a Fivetran connector
+- **A machine learning model**, such as TensorFlow or PyTorch
+- **An asset from an integration,** such as a dbt model or a Fivetran connector
 
 Assets aren’t limited to just the objects listed above - these are just some common examples.
 

docs/dagster-university/pages/dagster-essentials/lesson-4/coding-practice-taxi-zones-asset.md

@@ -30,7 +30,7 @@ The asset you built should look similar to the following code. Click **View answ
     deps=["taxi_zones_file"]
 )
 def taxi_zones() -> None:
-    sql_query = f"""
+    query = f"""
         create or replace table zones as (
             select
                 LocationID as zone_id,
@@ -42,5 +42,5 @@ def taxi_zones() -> None:
     """
 
     conn = duckdb.connect(os.getenv("DUCKDB_DATABASE"))
-    conn.execute(sql_query)
+    conn.execute(query)
 ```

docs/dagster-university/pages/dagster-essentials/lesson-4/loading-data-into-a-database.md

@@ -25,7 +25,7 @@ Now that you have a query that produces an asset, let’s use Dagster to manage
        """
          The raw taxi trips dataset, loaded into a DuckDB database
        """
-       sql_query = """
+       query = """
            create or replace table trips as (
              select
                VendorID as vendor_id,
@@ -43,7 +43,7 @@ Now that you have a query that produces an asset, let’s use Dagster to manage
        """
 
        conn = duckdb.connect(os.getenv("DUCKDB_DATABASE"))
-       conn.execute(sql_query)
+       conn.execute(query)
    ```
 
    Let’s walk through what this code does:
@@ -52,13 +52,13 @@ Now that you have a query that produces an asset, let’s use Dagster to manage
 
    2. The `taxi_trips_file` asset is defined as a dependency of `taxi_trips` through the `deps` argument.
 
-   3. Next, a variable named `sql_query` is created. This variable contains a SQL query that creates a table named `trips`, which sources its data from the `data/raw/taxi_trips_2023-03.parquet` file. This is the file created by the `taxi_trips_file` asset.
+   3. Next, a variable named `query` is created. This variable contains a SQL query that creates a table named `trips`, which sources its data from the `data/raw/taxi_trips_2023-03.parquet` file. This is the file created by the `taxi_trips_file` asset.
 
    4. A variable named `conn` is created, which defines the connection to the DuckDB database in the project. To do this, it uses the `.connect` method from the `duckdb` library, passing in the `DUCKDB_DATABASE` environment variable to tell DuckDB where the database is located.
 
       The `DUCKDB_DATABASE` environment variable, sourced from your project’s `.env` file, resolves to `data/staging/data.duckdb`. **Note**: We set up this file in Lesson 2 - refer to this lesson if you need a refresher. If this file isn’t set up correctly, the materialization will result in an error.
 
-   5. Finally, `conn` is paired with the DuckDB `execute` method, where our SQL query (`sql_query`) is passed in as an argument. This tells the asset that, when materializing, to connect to the DuckDB database and execute the query in `sql_query`.
+   5. Finally, `conn` is paired with the DuckDB `execute` method, where our SQL query (`query`) is passed in as an argument. This tells the asset that, when materializing, to connect to the DuckDB database and execute the query in `query`.
 
 3. Save the changes to the file.
 
@@ -98,9 +98,9 @@ This is because you’ve told Dagster that taxi_trips depends on the taxi_trips_
 To confirm that the `taxi_trips` asset materialized properly, you can access the newly made `trips` table in DuckDB. In a new terminal session, open a Python REPL and run the following snippet:
 
 ```python
-> import duckdb
-> conn = duckdb.connect(database="data/staging/data.duckdb") # assumes you're writing to the same destination as specified in .env.example
-> conn.execute("select count(*) from trips").fetchall()
+import duckdb
+conn = duckdb.connect(database="data/staging/data.duckdb") # assumes you're writing to the same destination as specified in .env.example
+conn.execute("select count(*) from trips").fetchall()
 ```
 
 The command should succeed and return a row count of the taxi trips that were ingested. When finished, make sure to stop the terminal process before continuing or you may encounter an error. Use `Control+C` or `Command+C` to stop the process.

docs/dagster-university/pages/dagster-essentials/lesson-6/using-resources-in-assets.md

@@ -31,7 +31,7 @@ from dagster import asset
     deps=["taxi_trips_file"],
 )
 def taxi_trips() -> None:
-    sql_query = """
+    query = """
         create or replace table taxi_trips as (
           select
             VendorID as vendor_id,
@@ -49,7 +49,7 @@ def taxi_trips() -> None:
     """
 
     conn = duckdb.connect(os.getenv("DUCKDB_DATABASE"))
-    conn.execute(sql_query)
+    conn.execute(query)
 ```
 
 ---
@@ -72,7 +72,7 @@ from dagster import asset
     deps=["taxi_trips_file"],
 )
 def taxi_trips(database: DuckDBResource) -> None:
-    sql_query = """
+    query = """
         create or replace table taxi_trips as (
           select
             VendorID as vendor_id,
@@ -90,7 +90,7 @@ def taxi_trips(database: DuckDBResource) -> None:
     """
 
     with database.get_connection() as conn:
-        conn.execute(sql_query)
+        conn.execute(query)
 ```
 
 To refactor `taxi_trips` to use the `database` resource, we had to:

docs/dagster-university/pages/dagster-essentials/lesson-7/what-are-schedules.md

@@ -92,7 +92,7 @@ Despite many schedulers and orchestrators replacing the cron program since then,
 
 Consider the following example:
 
-```python
+```
 15 5 * * 1-5
 ```
 

docs/dagster-university/pages/dagster-essentials/lesson-8/adding-partitions-to-assets.md

@@ -63,7 +63,7 @@ To add the partition to the asset:
    @asset(
        partitions_def=monthly_partition
    )
-   def taxi_trips_file(context) -> None:
+   def taxi_trips_file(context: AssetExecutionContext) -> None:
        partition_date_str = context.partition_key
    ```
 
@@ -73,7 +73,7 @@ To add the partition to the asset:
    @asset(
        partitions_def=monthly_partition
    )
-   def taxi_trips_file(context) -> None:
+   def taxi_trips_file(context: AssetExecutionContext) -> None:
        partition_date_str = context.partition_key
        month_to_fetch = partition_date_str[:-3]
    ```
@@ -86,7 +86,7 @@ from ..partitions import monthly_partition
 @asset(
     partitions_def=monthly_partition
 )
-def taxi_trips_file(context) -> None:
+def taxi_trips_file(context: AssetExecutionContext) -> None:
   """
       The raw parquet files for the taxi trips dataset. Sourced from the NYC Open Data portal.
   """

docs/dagster-university/pages/dagster-essentials/lesson-8/coding-practice-partition-taxi-trips.md

@@ -17,7 +17,7 @@ To practice what you’ve learned, partition the `taxi_trips` asset by month usi
   {% callout %}
   You’ll need to drop the existing `taxi_trips` because of the new `partition_date` column. In a Python REPL or scratch script, run the following:
 
-  ```yaml
+  ```
   import duckdb
   conn = duckdb.connect(database="data/staging/data.duckdb")
   conn.execute("drop table trips;")

docs/dagster-university/pages/dagster-essentials/lesson-8/creating-a-schedule-with-a-date-based-partition.md

@@ -36,12 +36,14 @@ To add partition to the job, make the following changes:
 The job should now look like this:
 
 ```python
-from dagster import define_asset_job, AssetSelection, AssetKey
+from dagster import define_asset_job, AssetSelection
 from ..partitions import monthly_partition
 
+trips_by_week = AssetSelection.assets("trips_by_week")
+
 trip_update_job = define_asset_job(
     name="trip_update_job",
     partitions_def=monthly_partition, # partitions added here
-    selection=AssetSelection.all() - AssetSelection.assets(["trips_by_week"])
+    selection=AssetSelection.all() - trips_by_week
 )
 ```