Improving Databricks Performance: Leveraging Partitioning, Delta Lake Transaction Logging, and Auto Optimize

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Intro

Databricks provides a powerful platform for big data processing and analytics. However, as your data volumes grow, optimizing performance is important. In this article, I will go over three key techniques to improve your performance: Partitioning, Delta Lake Transaction Logging, and Auto Optimize. We’ll dive into each of these with brief examples and explanations.

What is Databricks?

Databricks is a platform for data engineers, data analysts, data scientists, and big data professionals to work with vast amounts of data. Whether you are trying to ingest large volumes of data in batches or in real time streaming, Databricks offers a way to manage data, build analytics, and develop ML solutions under one hood. The official description on the Databricks website explains the platform as so:

“Databricks is a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale. The Databricks Data Intelligence Platform integrates with cloud storage and security in your cloud account, and manages and deploys cloud infrastructure on your behalf.”