Deploy workloads with Azure Databricks Lakeflow jobs

Azure Databricks Lakeflow jobs provide a robust platform for deploying workloads efficiently. With features like Azure Databricks Jobs and Delta Live Tables, users can orchestrate complex data processing, machine learning, and analytics pipelines.

This lab will take approximately 40 minutes to complete.

Note: The Azure Databricks user interface is subject to continual improvement. The user interface may have changed since the instructions in this exercise were written.

Provision an Azure Databricks workspace

Tip: If you already have an Azure Databricks workspace, you can skip this procedure and use your existing workspace.

This exercise includes a script to provision a new Azure Databricks workspace. The script attempts to create a Premium tier Azure Databricks workspace resource in a region in which your Azure subscription has sufficient quota for the compute cores required in this exercise; and assumes your user account has sufficient permissions in the subscription to create an Azure Databricks workspace resource. If the script fails due to insufficient quota or permissions, you can try to create an Azure Databricks workspace interactively in the Azure portal.

  1. In a web browser, sign into the Azure portal at https://portal.azure.com.

  2. Use the [>_] button to the right of the search bar at the top of the page to create a new Cloud Shell in the Azure portal, selecting a PowerShell environment. The cloud shell provides a command line interface in a pane at the bottom of the Azure portal, as shown here:

    Azure portal with a cloud shell pane

    Note: If you have previously created a cloud shell that uses a Bash environment, switch it to PowerShell.

  3. Note that you can resize the cloud shell by dragging the separator bar at the top of the pane, or by using the —, ⤢, and X icons at the top right of the pane to minimize, maximize, and close the pane. For more information about using the Azure Cloud Shell, see the Azure Cloud Shell documentation.

  4. In the PowerShell pane, enter the following commands to clone this repo:

     rm -r mslearn-databricks -f
 git clone https://github.com/MicrosoftLearning/mslearn-databricks

  5. After the repo has been cloned, enter the following command to run the setup.ps1 script, which provisions an Azure Databricks workspace in an available region:

     ./mslearn-databricks/setup.ps1

  6. If prompted, choose which subscription you want to use (this will only happen if you have access to multiple Azure subscriptions).

  7. Wait for the script to complete - this typically takes around 5 minutes, but in some cases may take longer. While you are waiting, review the Lakeflow jobs article in the Azure Databricks documentation.

Open the Azure Databricks Workspace

  1. In the Azure portal, browse to the msl-xxxxxxx resource group that was created by the script (or the resource group containing your existing Azure Databricks workspace)

  2. Select your Azure Databricks Service resource (named databricks-xxxxxxx if you used the setup script to create it).

  3. In the Overview page for your workspace, use the Launch Workspace button to open your Azure Databricks workspace in a new browser tab; signing in if prompted.

    Tip: As you use the Databricks Workspace portal, various tips and notifications may be displayed. Dismiss these and follow the instructions provided to complete the tasks in this exercise.

Create a notebook

  1. In the sidebar, use the (+) New link to create a Notebook.

  2. Change the default notebook name (Untitled Notebook [date]) to ETL task and in the Connect drop-down list, select Serverless compute if it is not already selected. If the compute is not running, it may take a minute or so to start.

Ingest data

  1. In the first cell of the notebook, enter the following code to create a volume for storing some lab files.

     %sql
 CREATE VOLUME IF NOT EXISTS spark_lab

  2. Add a new code cell and use it to run the following code, which uses Python to download data files from GitHub into your volume.

     import requests

 # Define the current catalog
 catalog_name = spark.sql("SELECT current_catalog()").collect()[0][0]

 # Define the base path using the current catalog
 volume_base = f"/Volumes/{catalog_name}/default/spark_lab"

 # List of files to download
 files = ["2019.csv", "2020.csv", "2021.csv"]

 # Download each file
 for file in files:
     url = f"https://raw.githubusercontent.com/MicrosoftLearning/mslearn-databricks/main/data/{file}"
     response = requests.get(url)
     response.raise_for_status()

     # Write to Unity Catalog volume
     with open(f"{volume_base}/{file}", "wb") as f:
         f.write(response.content)
  3. Use the ▸ Run Cell menu option at the left of the cell to run it. Then wait for the Spark job run by the code to complete.

  4. Under the output, use the + Code icon to add a new code cell, and use it to run the following code, which defines a schema for the data:

    from pyspark.sql.types import *
from pyspark.sql.functions import *
orderSchema = StructType([
     StructField("SalesOrderNumber", StringType()),
     StructField("SalesOrderLineNumber", IntegerType()),
     StructField("OrderDate", DateType()),
     StructField("CustomerName", StringType()),
     StructField("Email", StringType()),
     StructField("Item", StringType()),
     StructField("Quantity", IntegerType()),
     StructField("UnitPrice", FloatType()),
     StructField("Tax", FloatType())
])
df = spark.read.load(f'/Volumes/{catalog_name}/default/spark_lab/*.csv', format='csv', schema=orderSchema)
display(df.limit(100))

Create a job task

  1. Under the existing code cell, use the + Code icon to add a new code cell. Then in the new cell, enter and run the following code to remove duplicate rows and to replace the null entries with the correct values:

     from pyspark.sql.functions import col
 df = df.dropDuplicates()
 df = df.withColumn('Tax', col('UnitPrice') * 0.08)
 df = df.withColumn('Tax', col('Tax').cast("float"))

    Note: After updating the values in the Tax column, its data type is set to float again. This is due to its data type changing to double after the calculation is performed. Since double has a higher memory usage than float, it is better for performance to type cast the column back to float.

  2. In a new code cell, run the following code to aggregate and group the order data:

    yearlySales = df.select(year("OrderDate").alias("Year")).groupBy("Year").count().orderBy("Year")
display(yearlySales)

Build the Workflow

Azure Databricks manages the task orchestration, cluster management, monitoring, and error reporting for all of your jobs. You can run your jobs immediately, periodically through an easy-to-use scheduling system, whenever new files arrive in an external location, or continuously to ensure an instance of the job is always running.

  1. In your workspace, click Workflows icon. Jobs & Pipelines in the sidebar.

  2. In the Jobs & Pipelines pane, select Create, then Job.

  3. Change the default job name (New job [date]) to ETL job.

  4. Configure the job with the following settings:
    • Task name: Run ETL task notebook
    • Type: Notebook
    • Source: Workspace
    • Path: Select your ETL task notebook
    • Cluster: Select Serverless

  5. Select Create task.

  6. Select Run now.

  7. After the job starts running, you can monitor its execution by selecting Job Runs in the left sidebar.

  8. After the job run succeeds, you can select it and verify its output.

Additionally, you can run jobs on a triggered basis, for example, running a workflow on a schedule. To schedule a periodic job run, you can open the job task and add a trigger.

Clean up

If you’ve finished exploring Azure Databricks, you can delete the resources you’ve created to avoid unnecessary Azure costs and free up capacity in your subscription.