Summary
In this chapter, we learned about several optimization techniques concerning Spark Core. We started off by learning about broadcast joins and how they are more performant than a standard join. Then, we learned about the advantages of using Apache Arrow with Pandas. Next, we learned about shuffle partitions and Spark caching.
Finally, we learned about AQE and how it helps to speed up queries during runtime. All these optimization techniques are highly useful for tuning big data workloads in Azure Databricks.
In the next chapter, we will learn about real-world case studies with Databricks. We will learn about modern-day solution architectures using Azure Databricks across different industries and sectors.
