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Data Engineering with Databricks Cookbook

Data Engineering with Databricks Cookbook

By : Pulkit Chadha
4.4 (7)
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Data Engineering with Databricks Cookbook

Data Engineering with Databricks Cookbook

4.4 (7)
By: Pulkit Chadha

Overview of this book

Written by a Senior Solutions Architect at Databricks, Data Engineering with Databricks Cookbook will show you how to effectively use Apache Spark, Delta Lake, and Databricks for data engineering, starting with comprehensive introduction to data ingestion and loading with Apache Spark. What makes this book unique is its recipe-based approach, which will help you put your knowledge to use straight away and tackle common problems. You’ll be introduced to various data manipulation and data transformation solutions that can be applied to data, find out how to manage and optimize Delta tables, and get to grips with ingesting and processing streaming data. The book will also show you how to improve the performance problems of Apache Spark apps and Delta Lake. Advanced recipes later in the book will teach you how to use Databricks to implement DataOps and DevOps practices, as well as how to orchestrate and schedule data pipelines using Databricks Workflows. You’ll also go through the full process of setup and configuration of the Unity Catalog for data governance. By the end of this book, you’ll be well-versed in building reliable and scalable data pipelines using modern data engineering technologies.
Table of Contents (16 chapters)
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1
Part 1 – Working with Apache Spark and Delta Lake
9
Part 2 – Data Engineering Capabilities within Databricks

Performance Tuning with Apache Spark

Apache Spark is a powerful and versatile framework for large-scale data processing. It offers high-level APIs in Scala, Java, Python, and R, as well as low-level access to the Spark core engine. Spark supports a variety of workloads, such as batch processing, streaming, machine learning, graph analytics, and SQL queries. However, to get the most out of Spark, you need to know how to optimize its performance and avoid common pitfalls.

In this chapter, you will learn how to performance-tune Apache Spark applications.

We will cover the following recipes in this chapter:

  • Monitoring Spark jobs in the Spark UI
  • Using broadcast variables
  • Optimizing Spark jobs by minimizing data shuffling
  • Avoiding data skew
  • Caching and persistence
  • Partitioning and repartitioning
  • Optimizing join strategies

By the end of this chapter, you will have a solid understanding of how to tune Apache Spark for optimal performance and how...

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