Iโm currently analyzing a large geospatial dataset focused on Michigan county boundaries and map data, and Iโm using Apache Spark on Databricks to process and transform millions of records.
Even though Iโve optimized basic things like repartitioning, using cache(), and adjusting cluster size, my jobs still take a long time to complete โ 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
Iโd 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, Iโm working with a dataset similar to whatโs publicly available on Michigan County Map, focusing on region-based insights and boundary-level processing.
https://michigancountymap.com/
