Optimizing Performance with Delta Tables: A Guide to Merge and Copy Into Commands

Delta Lake is a powerful storage layer that enables scalable, reliable, and performant data pipelines on top of Apache Spark. In the previous article, we discussed <a href="https://medium.com/@vivekjadhavr/delta-lake-insert-overwrite-vs-insert-into-for-efficient-table-insertion-cb8a02d15909" rel="noopener">INSERT OVERWRITE vs INSERT INTO for Efficient Table Insertion</a> One of the key features of Delta Lake is the ability to perform atomic, scalable, and high-performance updates and merges of data through its Merge and Copy Into commands. In this article, we will explore these two commands and their performance implications on Delta Tables. <h2>Merge Command</h2> The Merge command allows you to merge data from one table or data frame into another table based on a join condition. The Merge command supports three types of operations: insert, update, and delete. Here is an example of a Merge command that updates a Delta Table called <code>sales</code> with new data from another Delta Table called <code>new_sales</code>: <a href="https://vivekjadhavr.medium.com/optimizing-performance-with-delta-tables-a-guide-to-merge-and-copy-into-commands-ab5cbf46b864">Click Here</a>