Building Modern Data Applications Using Databricks Lakehouse . - Will Girten; PACKT (GB: Packt, 2024)
Get up to speed with the Databricks Data Intelligence Platform to build and scale modern data applications, leveraging the latest advancements in data engineering Key Features Learn how to work with real-time data using Delta Live Tables Unlock insights into the performance of data pipelines using Delta Live Tables Apply your knowledge to Unity Catalog for robust data security and governance Purchase of the print or Kindle book includes a free PDF eBook Book Description With so many tools to choose from in today's data engineering development stack as well as operational complexity, this often overwhelms data engineers, causing them to spend less time gleaning value from their data and more time maintaining complex data pipelines. Guided by a lead specialist solutions architect at Databricks with 10+ years of experience in data and AI, this book shows you how the Delta Live Tables framework simplifies data pipeline development by allowing you to focus on defining input data sources, transformation logic, and output table destinations. This book gives you an overview of the Delta Lake format, the Databricks Data Intelligence Platform, and the Delta Live Tables framework. It teaches you how to apply data transformations by implementing the Databricks medallion architecture and continuously monitor the data quality of your pipelines. You'll learn how to handle incoming data using the Databricks Auto Loader feature and automate real-time data processing using Databricks workflows. You'll master how to recover from runtime errors automatically. By the end of this book, you'll be able to build a real-time data pipeline from scratch using Delta Live Tables, leverage CI/CD tools to deploy data pipeline changes automatically across deployment environments, and monitor, control, and optimize cloud costs. What you will learn Deploy near-real-time data pipelines in Databricks using Delta Live Tables Orchestrate data pipelines using Databricks workflows Implement data validation policies and monitor/quarantine bad data Apply slowly changing dimensions (SCD), Type 1 and 2, data to lakehouse tables Secure data access across different groups and users using Unity Catalog Automate continuous data pipeline deployment by integrating Git with build tools such as Terraform and Databricks Asset Bundles Who this book is for This book is for data engineers looking to streamline data ingestion, transformation, and orchestration tasks. Data analysts responsible for managing and processing lakehouse data for analysis, reporting, and visualization will also find this book beneficial. Additionally, DataOps/DevOps engineers will find this book helpful for automating the testing and deployment of data pipelines, optimizing table tasks, and tracking data lineage within the lakehouse. Beginner-level knowledge of Apache Spark and Python is needed to make the most out of this book.
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