Data Engineering with Databricks Cookbook: Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake

by Pulkit Chadha

Over 90 recipes to learn how to implement reliable data pipelines with Apache Spark, optimally store and process structured and unstructured data in Delta Lake and use Databricks to orchestrate and govern your data. Apache Spark is a powerful open-source distributed computing system that enables fast and flexible data processing and Delta Lake is an open-source storage layer that provides reliability, performance, and scale for data lakes. This book will show you recipes for effectively using Apache Spark, Delta Lake, and Databricks for data engineering, beginning with an introduction to data ingestion and loading with Apache Spark. You will be introduced to various data manipulation and data transformation solutions that can be applied to data. You'll discover how to manage and optimize Delta tables, as well as how to ingest and process streaming data. You'll learn how to improve the performance problems of Apache Spark apps and Delta Lake. Later chapters will teach you how to use Databricks to implement DataOps and DevOps practices. You'll then learn how to orchestrate and schedule data pipelines using Databricks Workflows. Finally, you will go over how to set up and configure Unity Catalog for data governance. By the end of this book, you’ll learn how to build reliable data pipelines with modern data engineering technologies as well as have a comprehensive understanding of how to build efficient and scalable data pipelines. This book is for data engineers and data practitioners who want to learn how to build efficient and scalable data pipelines using Apache Spark, Delta Lake, and Databricks. To get the most out of this book, you should have basic knowledge of Data Architecture, SQL, and Python

438 pages, Paperback

Published May 31, 2024