Azure Databricks Tutorial | Data transformations at scale

Introduction

This tutorial provides a comprehensive guide on using Azure Databricks for data transformations at scale. Azure Databricks is a powerful platform that simplifies big data processing and analytics. With its user-friendly interface and integration with Apache Spark, you can perform complex data transformations using Python or Scala scripts without deep technical knowledge. This guide focuses on transforming data from JSON format in Blob Storage to CSV format in Blob Storage, a common scenario for data engineers.

Step 1: Setting Up Azure Databricks

• Create an Azure Databricks Workspace

  • Log in to your Azure portal.
  • Search for "Databricks" in the search bar.
  • Click on "Create" and fill in the necessary details like subscription, resource group, workspace name, and region.
  • Click on "Review + Create" and then "Create" to provision your workspace.

• Launch Your Workspace

  • Once created, go to your Databricks workspace.
  • Click on "Launch Workspace" to access the Databricks environment.

Step 2: Importing Data from Blob Storage

• Set Up Blob Storage

  • Ensure you have your Blob Storage account set up in Azure with the JSON files you want to transform.

• Mount Blob Storage in Databricks

  • Use the following code snippet in a Databricks notebook to mount the Blob Storage:

    dbutils.fs.mount(
      source = "wasbs://<container-name>@<storage-account-name>.blob.core.windows.net/",
      mount_point = "/mnt/<mount-name>",
      extra_configs = {"<conf-key>":dbutils.secrets.get(scope = "<scope-name>", key = "<key-name>")}
    )
    

  • Replace <container-name>, <storage-account-name>, <mount-name>, <conf-key>, <scope-name>, and <key-name> with your specific details.

Step 3: Reading JSON Data

• Load JSON File into a DataFrame

  • Use the following code snippet to read the JSON data:

    json_df = spark.read.json("/mnt/<mount-name>/<json-file-name>.json")
    

  • Replace <json-file-name> with the name of your JSON file.

• Display DataFrame Contents

  • Use the command below to view the loaded data:

    json_df.show()
    

Step 4: Data Transformation

• Perform Necessary Transformations
  • Use DataFrame operations to transform your data as needed. For example, you can filter, select specific columns, or aggregate data.
  • Example of selecting specific columns:

    transformed_df = json_df.select("column1", "column2")
    

Step 5: Writing Data to Blob Storage in CSV Format

• Write the Transformed DataFrame to CSV
  • Use the following code to save the transformed DataFrame as a CSV file:

    transformed_df.write.csv("/mnt/<mount-name>/<output-file-name>.csv", header=True)
    

  • Replace <output-file-name> with your desired output file name.

Conclusion

In this tutorial, you learned how to set up Azure Databricks, import data from Blob Storage, and perform data transformations from JSON to CSV format. Azure Databricks streamlines the data processing workflow, making it accessible for users with varying levels of technical expertise. For further learning, consider exploring Azure Databricks documentation, online modules, and additional resources to enhance your skills in data engineering.

Next steps:

• Explore the Azure Databricks documentation for deeper insights.
• Check out online learning modules to continue your education.
• Experiment with different data transformation scenarios to build your expertise.