Data Engineering with Databricks Cookbook (eBook)

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.

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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 dataKey FeaturesLearn data ingestion, data transformation, and data management techniques using Apache Spark and Delta LakeGain practical guidance on using Delta Lake tables and orchestrating data pipelinesImplement reliable DataOps and DevOps practices, and enforce data governance policies on DatabricksPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData 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 learnPerform data loading, ingestion, and processing with Apache SparkDiscover data transformation techniques and custom user-defined functions (UDFs) in Apache SparkManage and optimize Delta tables with Apache Spark and Delta Lake APIsUse Spark Structured Streaming for real-time data processingOptimize Apache Spark application and Delta table query performanceImplement DataOps and DevOps practices on DatabricksOrchestrate data pipelines with Delta Live Tables and Databricks WorkflowsImplement data governance policies with Unity CatalogWho this book is forThis 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.]]>