Databricks Performance Optimization

(Practical Techniques to Make Your Workloads Faster and More Cost-Efficient)

Have you ever run a Databricks job that takes hours to complete, consumes more compute than expected, or becomes slower as your data grows?

If you work with large-scale data pipelines, you have probably faced these challenges. Increasing the number of workers or choosing a larger cluster might seem like the easiest solution, but better performance doesn’t always come from adding more compute.

Databricks performance optimization is about understanding your workload, identifying bottlenecks, and making informed decisions about data processing, Spark execution, storage, and compute resources.

In this article, I’ll explore practical techniques to optimize Databricks workloads, improve execution time, and control costs focusing on the changes that can make a real difference in day-to-day data engineering.

Let’s dive in one by one!

1. Over Partitioning

We see many data engineers partitioning their tables in ways that can cause major performance issues, without improving future query performance. This is called “over partitioning”.

• Disable Caching