{"id":2894,"date":"2018-04-02T00:00:00","date_gmt":"2018-04-02T00:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/blog\/operationalize-azure-databricks-notebooks-using-data-factory"},"modified":"2025-07-01T08:18:40","modified_gmt":"2025-07-01T15:18:40","slug":"operationalize-azure-databricks-notebooks-using-data-factory","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/operationalize-azure-databricks-notebooks-using-data-factory\/","title":{"rendered":"Ingest, prepare, and transform using Azure Databricks and Data Factory"},"content":{"rendered":"\n

Today\u2019s business managers depend heavily on reliable data integration systems that run complex ETL\/ELT workflows (extract, transform\/load and load\/transform data). These workflows allow businesses to ingest data in various forms and shapes from different on-prem\/cloud data sources; transform\/shape the data and gain actionable insights into data to make important business decisions.<\/p>\n\n\n\n

With the general availability<\/a> of Azure Databricks comes support for doing ETL\/ELT with Azure Data Factory<\/a>.<\/p>\n\n\n\n

This integration allows you to operationalize ETL\/ELT workflows (including analytics workloads in Azure Databricks) using data factory pipelines that do the following:<\/p>\n\n\n\n

    \n
  1. Ingest data <\/a>at scale using 70+ on-prem\/cloud data sources<\/li>\n\n\n\n
  2. Prepare and transform (clean, sort, merge, join, etc.) the ingested data in Azure Databricks as a Notebook<\/a> activity step in data factory pipelines<\/li>\n\n\n\n
  3. Monitor and manage<\/a> your E2E workflow<\/li>\n<\/ol>\n\n\n\n
    \"image\"<\/figure>\n\n\n\n

    Take a look at a sample data factory pipeline where we are ingesting data from Amazon S3 to Azure Blob, processing the ingested data using a Notebook running in Azure Databricks and moving the processed data in Azure SQL Datawarehouse.<\/p>\n\n\n\n

    \"image\"<\/figure>\n\n\n\n

    You can parameterize the entire workflow (folder name, file name, etc.) using rich expression support<\/a> and operationalize by defining a trigger<\/a> in data factory.<\/p>\n\n\n\n

    Get started today!<\/h2>\n\n\n\n

    We are excited for you to try Azure Databricks and Azure Data Factory integration and let us know your feedback.<\/p>\n\n\n\n

    Get started by clicking the Author & Monitor<\/strong> tile in your provisioned v2 data factory blade.<\/p>\n\n\n\n

    \"image\"<\/figure>\n\n\n\n

    Click on the Transform data with Azure Databricks tutorial and learn step by step how to operationalize your ETL\/ELT workloads including analytics workloads in Azure Databricks using Azure Data Factory.<\/p>\n\n\n\n

    \"image\"<\/figure>\n\n\n\n

    We are continuously working to add new features based on customer feedback. Get more information and detailed steps for using the Azure Databricks and Data Factory integration<\/a>.<\/p>\n\n\n\n

    Get started<\/a> building pipelines easily and quickly using Azure Data Factory. If you have any feature requests or want to provide feedback, please visit the Azure Data Factory forum<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

    Azure Databricks general availability was announced on March 22, 2018. Now Azure Databricks is fully integrated with Azure Data Factory (ADF).<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ms_queue_id":[],"ep_exclude_from_search":false,"_classifai_error":"","_classifai_text_to_speech_error":"","_alt_title":"","footnotes":"","msx_community_cta_settings":[]},"categories":[1454,1474,1473],"tags":[48],"audience":[3054,3057,3055,3053,3056],"content-type":[],"product":[1821,1544],"tech-community":[],"topic":[],"coauthors":[617],"class_list":["post-2894","post","type-post","status-publish","format-standard","hentry","category-ai-machine-learning","category-analytics","category-databases","tag-big-data","audience-business-decision-makers","audience-data-professionals","audience-developers","audience-it-decision-makers","audience-it-implementors","product-azure-data-factory","product-azure-databricks","review-flag-1680286581-295","review-flag-1680286581-56","review-flag-1680286581-364","review-flag-gener-1680286584-335","review-flag-new-1680286579-546","review-flag-on-pr-1680286585-789"],"yoast_head":"\nIngest, prepare, and transform using Azure Databricks and Data Factory | Microsoft Azure Blog<\/title>\n<meta name=\"description\" content=\"Azure Databricks general availability was announced on March 22, 2018. 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