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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

Optimizing Spark jobs by minimizing data shuffling

In this recipe, you will learn how to optimize Spark jobs by minimizing data shuffling. Data shuffling is the process of transferring data across different partitions or nodes. Data shuffling can be expensive and time-consuming, as it involves network I/O, disk I/O, and serialization/deserialization of data. Therefore, minimizing data shuffling is one of the key techniques for optimizing Spark performance.

Some of the most common scenarios when data shuffling occurs are the following:

  • When you perform a join operation on two or more DataFrames: Joining requires shuffling data across partitions or nodes based on the join keys
  • When you perform a global aggregation operation on a DataFrame: Global aggregation requires shuffling data across partitions or nodes to compute some statistics for the whole DataFrame
  • When you perform a repartition or coalesce operation on a DataFrame: Repartitioning or coalescing requires shuffling...

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