Work through 70 recipes for implementing 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 Key Features Learn data ingestion, data transformation, and data management techniques using Apache Spark and Delta Lake Gain practical guidance on using Delta Lake tables and orchestrating data pipelines Implement reliable DataOps and DevOps practices, and enforce data governance policies on Databricks Purchase of the print or Kindle book includes a free PDF eBook Book Description Data Engineering with Databricks Cookbook will guide you through recipes to effectively use Apache Spark, Delta Lake, and Databricks for data engineering, beginning with an introduction to data ingestion and loading with Apache Spark. As you progress, you'll be introduced to various data manipulation and data transformation solutions that can be applied to data. You'll find out how to manage and optimize Delta tables, as well as how to ingest and process streaming data. The book will also show you how to improve the performance problems of Apache Spark apps and Delta Lake. Later chapters will show you how to use Databricks to implement DataOps and DevOps practices and teach you how to orchestrate and schedule data pipelines using Databricks Workflows. Finally, you'll understand how to set up and configure 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 technol... See more

Work through 70 recipes for implementing 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

Key Features
Learn data ingestion, data transformation, and data management techniques using Apache Spark and Delta Lake
Gain practical guidance on using Delta Lake tables and orchestrating data pipelines
Implement reliable DataOps and DevOps practices, and enforce data governance policies on Databricks
Purchase of the print or Kindle book includes a free PDF eBook
Book Description
Data Engineering with Databricks Cookbook will guide you through recipes to effectively use Apache Spark, Delta Lake, and Databricks for data engineering, beginning with an introduction to data ingestion and loading with Apache Spark.

As you progress, you'll be introduced to various data manipulation and data transformation solutions that can be applied to data. You'll find out how to manage and optimize Delta tables, as well as how to ingest and process streaming data. The book will also show you how to improve the performance problems of Apache Spark apps and Delta Lake. Later chapters will show you how to use Databricks to implement DataOps and DevOps practices and teach you how to orchestrate and schedule data pipelines using Databricks Workflows. Finally, you'll understand how to set up and configure 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.

What you will learn
Perform data loading, ingestion, and processing with Apache Spark
Discover data transformation techniques and custom user-defined functions (UDFs) in Apache Spark
Manage and optimize Delta tables with Apache Spark and Delta Lake APIs
Use Spark Structured Streaming for real-time data processing
Optimize Apache Spark application and Delta table query performance
Implement DataOps and DevOps practices on Databricks
Orchestrate data pipelines with Delta Live Tables and Databricks Workflows
Implement data governance policies with Unity Catalog
Who this book is for
This book is for data engineers, data scientists, 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 programming.