Hello
I am working on optimizing the performance of my data pipelines in the Matillion Data Productivity Cloud. I am particularly interested in strategies to improve load times & reduce resource usage during large-scale transformations.
Are there recommended practices for efficiently managing concurrent transformations, optimizing query execution & minimizing costs while handling dynamic workloads?
Additionally, I'd like to understand how factors like job orchestration, caching / incremental data loads can influence overall performance.
Are there specific tools / configurations in Matillion that can help monitor & analyze bottlenecks in pipelines?
I checked out the https://www.matillion.com/blog/matillion-etl-job-performance-analysis-and-tuning-rpa automation anywhere guide but would appreciate further insights from the community on how to apply these techniques in more complex use cases.
Any tips would be greatly appreciated. I am open to exploring adjustments at the ETL design / cloud infrastructure level.
Thank you !