Book Image

Optimizing Databricks Workloads

By : Anirudh Kala, Anshul Bhatnagar, Sarthak Sarbahi
Book Image

Optimizing Databricks Workloads

By: Anirudh Kala, Anshul Bhatnagar, Sarthak Sarbahi

Overview of this book

Databricks is an industry-leading, cloud-based platform for data analytics, data science, and data engineering supporting thousands of organizations across the world in their data journey. It is a fast, easy, and collaborative Apache Spark-based big data analytics platform for data science and data engineering in the cloud. In Optimizing Databricks Workloads, you will get started with a brief introduction to Azure Databricks and quickly begin to understand the important optimization techniques. The book covers how to select the optimal Spark cluster configuration for running big data processing and workloads in Databricks, some very useful optimization techniques for Spark DataFrames, best practices for optimizing Delta Lake, and techniques to optimize Spark jobs through Spark core. It contains an opportunity to learn about some of the real-world scenarios where optimizing workloads in Databricks has helped organizations increase performance and save costs across various domains. By the end of this book, you will be prepared with the necessary toolkit to speed up your Spark jobs and process your data more efficiently.
Table of Contents (13 chapters)
1
Section 1: Introduction to Azure Databricks
5
Section 2: Optimization Techniques
10
Section 3: Real-World Scenarios

Chapter 6: Databricks Delta Lake

Delta Lake is an open source storage layer that provides functionalities to data in the data lake that only exist in data warehouses. When combined with cloud storage, Databricks and Delta Lake lead to the formation of a Lakehouse. A Lakehouse simply provides the best of both worlds – data lakes and data warehouses. In today's world, a Lakehouse provides the same set of capabilities as a traditional data warehouse and at a much lower cost. This is made possible due to cheap cloud storage such as Azure Data Lake, Spark as the processing engine, and data being stored in the Delta Lake format. In this chapter, we will learn about various Delta Lake optimizations that help us build a more performant Lakehouse.

In this chapter, we will cover the following topics:

  • Working with the OPTIMIZE and ZORDER commands
  • Using AUTO OPTIMIZE
  • Learning about delta caching
  • Learning about dynamic partition pruning
  • Understanding bloom...