December 2021
Intermediate to advanced
230 pages
4h 17m
English
Content preview from Optimizing Databricks Workloads
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Chapter 7: Spark Core
Performance tuning in Apache Spark plays an instrumental role in running efficient big data workloads. More often than not, the optimization techniques employed to prevent the shuffling and skewing of data drastically improve performance. In this chapter, we will learn about the Spark optimization techniques directly related to Spark Core that help prevent the shuffling and skewing of data.
We will begin by learning about broadcast joins and how they are different from traditional joins in Spark. Next, we will learn about Apache Arrow, its integration with the Python pandas project, and how it improves the performance of Pandas code in Azure Databricks. We will also learn about shuffle partitions, Spark caching, and adaptive ...
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