How to Optimize Spark Jobs in Databricks for Large-Scale Geospatial Data Processing?

kristym — Mon, 20 Oct 2025 11:50:29 GMT

I’m currently analyzing a large geospatial dataset focused on Michigan county boundaries and map data, and I’m using Apache Spark on Databricks to process and transform millions of records.

Even though I’ve optimized basic things like repartitioning, using cache(), and adjusting cluster size, my jobs still take a long time to complete — especially during wide transformations and joins across multiple data sources.

What are the most effective techniques or configurations in Databricks to:

Improve job performance for large datasets
Handle shuffle operations more efficiently
Optimize joins and partitioning for geospatial or map-based data
Reduce memory overhead or out-of-memory errors
Take advantage of Delta Lake features for faster queries

I’d also love to learn if there are real-world examples or tuning guides for handling map-style datasets (like county-level data) efficiently.

For context, I’m working with a dataset similar to what’s publicly available on Michigan County Map, focusing on region-based insights and boundary-level processing.

Re: How to Optimize Spark Jobs in Databricks for Large-Scale Geospatial Data Processing?

-werners- — Mon, 20 Oct 2025 13:04:04 GMT

I do not have experience with geospatial data on databricks.
But I do know that since a while, Sedona can be installed on Databricks.
Sedona is created for large-scale geospatial data processing. Sounds like something for you no?

https://sedona.apache.org/latest/setup/databricks/

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How to Optimize Spark Jobs in Databricks for Large-Scale Geospatial Data Processing?

Re: How to Optimize Spark Jobs in Databricks for Large-Scale Geospatial Data Processing?