Get up to speed with the Databricks Data Intelligence Platform to build and scale modern data applications, leveraging the latest advancements in data engineeringKey FeaturesLearn how to work with real-time data using Delta Live TablesUnlock insights into the performance of data pipelines using Delta Live TablesApply your knowledge to Unity Catalog for robust data security and governancePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWith 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 learnDeploy near-real-time data pipelines in Databricks using Delta Live TablesOrchestrate data pipelines using Databricks workflowsImplement data validation policies and monitor/quarantine bad dataApply slowly changing dimensions (SCD), Type 1 and 2, data to lakehouse tablesSecure data access across different groups and users using Unity CatalogAutomate continuous data pipeline deployment by integrating Git with build tools such as Terraform and Databricks Asset BundlesWho this book is forThis 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