Summary
Like the last chapter, this chapter also introduced a lot of new concepts. Some of these concepts will take a long time to master, such as Spark debugging, optimizing shuffle partitions, and identifying and reducing data spills. These topics could be separate books on their own. I've tried my best to give you an overview of these topics with follow-up links. Please go through the links to learn more about them.
Let's recap what we learned in this chapter. We started with data compaction as small files are very inefficient in big data analytics. We then learned about UDFs, and how to handle data skews and data spills in both SQL and Spark. We then explored shuffle partitions in Spark. We learned about using indexers and cache to speed up our query performance. We also learned about HTAP, which was a new concept that merges OLAP and OLTP processing. We then explored the general resource management tips for descriptive and analytical platforms. And finally, we wrapped...
