[0:05]In a project where we had to process large amounts of financial data from multiple sources, the initial pipeline took too long to execute due to the volume of data.
[0:10]To optimize this, enabled parallelism by setting up multiple copy activities to run concurrently, each handling a different data set partition.
[0:15]Use the staging feature in the copy activity to temporarily buffer the data in Azure blob storage before processing it further, significantly improving throughput.
[0:21]Also use data flow optimizations by caching lookup tables used in transformations. These adjustments improves the pipeline's performance by 40%, reducing execution time.
