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Data Engineering
Join discussions on data engineering best practices, architectures, and optimization strategies within the Databricks Community. Exchange insights and solutions with fellow data engineers.
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How can I extract data from different sources and transform it into a fresh, reliable data pipeline?

Valued Contributor

Tip: These steps are built out for AWS accounts and workspaces that are using Delta Lake. If you would like to learn more watch this video and reach out to your Databricks sales representative for more information.

Step 1: Create your own notebook or use an existing notebook

Step 2: Ensure your data is ingested into the lakehouse

Step 3: Efficiently read and write your data

Step 4: Automate your notebook with a job

Step 5: Run your job interactively or on a schedule

Step 6: Further optimize your data pipeline with delta write optimize & multi-task jobs

If you'd like to learn more, sign up for the Databricks Data Engineering Databricks Academy course and get your Data Engineering Certification.

Need more help? Get hands-on with building data pipelines by attending the Data Engineering Activation Day on December 6, 2022, at 9 AM PT. Whether you are just getting started ingesting data or you are ready to automate your data pipeline, our Databricks experts will walk you through each step of the way with ample time for your questions.

In this session, you’ll see demos and learn:

- How to quickly create your own notebook

- All ways to ingest your data

- Best practices to read and write your data

- Automated ways to run your commands with Jobs

Notebooks will be provided so you can follow along live or review the best practices at your own pace. 


Esteemed Contributor III

thanks man


If you are using Azure Databricks prefer ADF Pipelines for ETL.​

New Contributor III

Thanks for this.

Esteemed Contributor III

Thanks @Nithya Thangaraj​ 

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