How to Run an Azure Databricks Notebook with Azure Data Factory

Learn how to run an Azure Databricks notebook in Azure Data Factory, making it easier to automate big data processing tasks. This tutorial covers the setup and configuration of Azure Data Factory, the creation of a pipeline to run the Databricks notebook, and the scheduling and execution of the pipeline. With these skills, you'll be able to streamline your data processing workflows and achieve increased efficiency and scalability. Watch now to learn how to run an Azure Databricks notebook with Azure Data Factory.

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[afiqghazali]

Very straightforward and helpful. Kudos

[apurvgolatgaonkar-]

thanks sir your video is very helpful for me. 🙂

[rakeshreddy]

Hello... I found your video to be very helpful for me. Let me know if you can provide me Azure Data Factory and Azure Databricks training.

[lorenzosvezia]

Ty for sharing this useful info I actually have a similar problem, I'm trying to create a service principal on Databricks but i don't understand how the tocken works, How does it works in that case?

[pigrebanto]

thanks. Does this work with Azure Blob Storage or Azure Data Lake?

[beaufonville]

how do you know where and what to mount?

[Gowtham-hmfr]

Task: Set up a Basic Data Pipeline in Azure
Step 1: Data Ingestion
Azure Service: Azure Event Hubs or Azure Blob Storage
Steps:
1. Create an Azure Event Hub namespace or Blob Storage account.
2. Set up an Event Hub or Blob Container to receive incoming data.
3. Configure access policies and keys for ingestion.

Step 2: Data Transformation
Azure Service: Azure Databricks or Azure HDInsight (Spark)
Steps:
1. Provision an Azure Databricks workspace or HDInsight cluster.
2. Develop a Py Spark or Spark job to process and transform data.
3. Schedule or manually run the Spark job to process incoming data.

Step 3: Data Storage
Azure Service: Azure Data Lake Storage Gen2 (ADLS Gen2) or Azure SQL Database
Steps:
1. Create an ADLS Gen2 storage account or Azure SQL Database.
2. Define folders or tables to store processed data.
3. Ensure proper access control and data retention policies.

Step 4: Orchestration and Monitoring
Azure Service: Azure Data Factory
Steps:
1. Set up an Azure Data Factory (ADF) instance.
2. Create pipelines to orchestrate data movement from Event Hub/Blob to Databricks/HDInsight to the ADLS Gen2/SQL Database.
3. Configure triggers for pipeline execution and monitoring through ADF.



This is my task how to do that? Any specific video having that task please share me