Deploy workloads with Azure Databricks Workflows

Azure Databricks Workflows 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.

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 and creating storage if prompted. 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, use the drop-down menu at the top left of the cloud shell pane to change 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 Introduction to Delta Lake article in the Azure Databricks documentation.

Create a cluster

Azure Databricks is a distributed processing platform that uses Apache Spark clusters to process data in parallel on multiple nodes. Each cluster consists of a driver node to coordinate the work, and worker nodes to perform processing tasks. In this exercise, you’ll create a single-node cluster to minimize the compute resources used in the lab environment (in which resources may be constrained). In a production environment, you’d typically create a cluster with multiple worker nodes.

Tip: If you already have a cluster with a 13.3 LTS or higher runtime version in your Azure Databricks workspace, you can use it to complete this exercise and skip this procedure.

  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.

  4. In the sidebar on the left, select the (+) New task, and then select Cluster.

  5. In the New Cluster page, create a new cluster with the following settings:
    • Cluster name: User Name’s cluster (the default cluster name)
    • Policy: Unrestricted
    • Cluster mode: Single Node
    • Access mode: Single user (with your user account selected)
    • Databricks runtime version: 13.3 LTS (Spark 3.4.1, Scala 2.12) or later
    • Use Photon Acceleration: Selected
    • Node type: Standard_DS3_v2
    • Terminate after 20 minutes of inactivity

  6. Wait for the cluster to be created. It may take a minute or two.

    Note: If your cluster fails to start, your subscription may have insufficient quota in the region where your Azure Databricks workspace is provisioned. See CPU core limit prevents cluster creation for details. If this happens, you can try deleting your workspace and creating a new one in a different region. You can specify a region as a parameter for the setup script like this: ./mslearn-databricks/setup.ps1 eastus

Create a notebook and ingest data

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

  2. In the Connect drop-down list, select your cluster if it is not already selected. If the cluster is not running, it may take a minute or so to start.

  3. In the first cell of the notebook, enter the following code, which uses shell commands to download data files from GitHub into the file system used by your cluster.

     %sh
 rm -r /dbfs/workflow_lab
 mkdir /dbfs/workflow_lab
 wget -O /dbfs/workflow_lab/2019.csv https://github.com/MicrosoftLearning/mslearn-databricks/raw/main/data/2019_edited.csv
 wget -O /dbfs/workflow_lab/2020.csv https://github.com/MicrosoftLearning/mslearn-databricks/raw/main/data/2020_edited.csv
 wget -O /dbfs/workflow_lab/2021.csv https://github.com/MicrosoftLearning/mslearn-databricks/raw/main/data/2021_edited.csv

  4. 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.

Create a job task

You implement your data processing and analysis workflow using tasks. A job is composed of one or more tasks. You can create job tasks that run notebooks, JARS, Delta Live Tables pipelines, or Python, Scala, Spark submit, and Java applications. In this exercise, you will create a task as a notebook that extracts, transforms, and loads data into visualization charts.

  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 your cluster if it is not already selected. If the cluster is not running, it may take a minute or so to start.

  3. In the first cell of the notebook, enter the following code, which defines a schema for the data and loads the datasets in a dataframe:

    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('/workflow_lab/*.csv', format='csv', schema=orderSchema)
display(df.limit(100))

  4. Under the existing code cell, use the + 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.

  5. 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)

  6. Above the table of results, select + and then select Visualization to view the visualization editor, and then apply the following options:

    General tab:

    • Visualization type: Bar
    • X Column: Year
    • Y Column: Add a new column and select count. Apply the Sum aggregation.

    X axis tab:

    • Scale: Categorical

  7. Select Save.

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 the left sidebar, select Workflows.

  2. In the Workflows pane, select Create job.

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

  4. In the Task name field, enter a name for the task.

  5. In the Type drop-down menu, select Notebook.

  6. In the Path field, select your ETL task notebook.

  7. Select Create task.

  8. Select Run now.

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

  10. 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 select Add trigger in the right side panel.

Workflow task pane

Clean up

In Azure Databricks portal, on the Compute page, select your cluster and select ■ Stop to shut it down.

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.