Charlie Francis Training System

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 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 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. ]]> Title Page Copyright and Credits Dedication Contributors Table of Contents Preface Part 1 – Working with Apache Spark and Delta Lake Chapter 1: Data Ingestion and Data Extraction with Apache Spark Technical requirements Reading CSV data with Apache Spark How to do it... There’s more... See also Reading JSON data with Apache Spark How to do it... There’s more... See also Reading Parquet data with Apache Spark How to do it... See also Parsing XML data with Apache Spark How to do it... There’s more... See also Working with nested data structures in Apache Spark How to do it... There’s more... See also Processing text data in Apache Spark How to do it... There’s more... See also Writing data with Apache Spark How to do it... There’s more... See also Chapter 2: Data Transformation and Data Manipulation with Apache Spark Technical requirements Applying basic transformations to data with Apache Spark How to do it... There’s more... See also Filtering data with Apache Spark How to do it... There’s more... See also Performing joins with Apache Spark How to do it... There’s more... See also Performing aggregations with Apache Spark How to do it... There’s more... See also Using window functions with Apache Spark How to do it... There’s more... Writing custom UDFs in Apache Spark How to do it... There’s more... See also Handling null values with Apache Spark How to do it... There’s more... See also Chapter 3: Data Management with Delta Lake Technical requirements Creating a Delta Lake table How to do it... There’s more... See also Reading a Delta Lake table How to do it... There’s more... See also Updating data in a Delta Lake table How to do it... See also Merging data into Delta tables How to do it... There’s more... See also Change data capture in Delta Lake How to do it... See also Optimizing Delta Lake tables How to do it... There’s more... See also Versioning and time travel for Delta Lake tables How to do it... There’s more... See also Managing Delta Lake tables How to do it... See also Chapter 4: Ingesting Streaming Data Technical requirements Configuring Spark Structured Streaming for real-time data processing Getting ready How to do it... How it works... There’s more... See also Reading data from real-time sources, such as Apache Kafka, with Apache Spark Structured Streaming Getting ready How to do it... How it works... There’s more... See also Defining transformations and filters on a Streaming DataFrame Getting ready How to do it... See also Configuring checkpoints for Structured Streaming in Apache Spark Getting ready How to do it... How it works... There’s more... See also Configuring triggers for Structured Streaming in Apache Spark Getting ready How to do it... How it works... See also Applying window aggregations to streaming data with Apache Spark Structured Streaming Getting ready How to do it... There’s more... See also Handling out-of-order and late-arriving events with watermarking in Apache Spark Structured Streaming Getting ready How to do it... There’s more... See also Chapter 5: Processing Streaming Data Technical requirements Writing the output of Apache Spark Structured Streaming to a sink such as Delta Lake Getting ready How to do it... How it works... See also Idempotent stream writing with Delta Lake and Apache Spark Structured Streaming Getting ready How to do it... See also Merging or applying Change Data Capture on Apache Spark Structured Streaming and Delta Lake Getting ready How to do it... There’s more... Joining streaming data with static data in Apache Spark Structured Streaming and Delta Lake Getting ready How to do it... There’s more... See also Joining streaming data with streaming data in Apache Spark Structured Streaming and Delta Lake Getting ready How to do it... There’s more... See also Monitoring real-time data processing with Apache Spark Structured Streaming Getting ready How to do it... There’s more... See also Chapter 6: Performance Tuning with Apache Spark Technical requirements Monitoring Spark jobs in the Spark UI How to do it... See also Using broadcast variables How to do it... How it works... There’s more... Optimizing Spark jobs by minimizing data shuffling How to do it... See also Avoiding data skew How to do it... There’s more... Caching and persistence How to do it... There’s more... Partitioning and repartitioning How to do it... There’s more... Optimizing join strategies How to do it... See also Chapter 7: Performance Tuning in Delta Lake Technical requirements Optimizing Delta Lake table partitioning for query performance How to do it... There’s more... See also Organizing data with Z-ordering for efficient query execution How to do it... How it works... See also Skipping data for faster query execution How to do it... See also Reducing Delta Lake table size and I/O cost with compression How to do it... How it works... See also Part 2 – Data Engineering Capabilities within Databricks Chapter 8: Orchestration and Scheduling Data Pipeline with Databricks Workflows Technical requirements Building Databricks workflows How to do it... See also Running and managing Databricks Workflows How to do it... See also Passing task and job parameters within a Databricks Workflow How to do it... See also Conditional branching in Databricks Workflows How to do it... See also Triggering jobs based on file arrival Getting ready How to do it... See also Setting up workflow alerts and notifications How to do it... There’s more... See also Troubleshooting and repairing failures in Databricks Workflows How to do it... See also Chapter 9: Building Data Pipelines with Delta Live Tables Technical requirements Creating a multi-hop medallion architecture data pipeline with Delta Live Tables in Databricks How to do it... How it works... See also Building a data pipeline with Delta Live Tables on Databricks How to do it... See also Implementing data quality and validation rules with Delta Live Tables in Databricks How to do it... How it works... See also Quarantining bad data with Delta Live Tables in Databricks How to do it... See also Monitoring Delta Live Tables pipelines How to do it... See also Deploying Delta Live Tables pipelines with Databricks Asset Bundles Getting ready How to do it... There’s more... See also Applying changes (CDC) to Delta tables with Delta Live Tables How to do it... See also Chapter 10: Data Governance with Unity Catalog Technical requirements Connecting to cloud object storage using Unity Catalog Getting ready How to do it... See also Creating and managing catalogs, schemas, volumes, and tables using Unity Catalog Getting ready How to do it... See also Defining and applying fine-grained access control policies using Unity Catalog Getting ready How to do it... See also Tagging, commenting, and capturing metadata about data and AI assets using Databricks Unity Catalog Getting ready How to do it... See also Filtering sensitive data with Unity Catalog Getting ready How to do it... See also Using Unity Catalogs lineage data for debugging, root cause analysis, and impact assessment Getting ready How to do it... See also Accessing and querying system tables using Unity Catalog Getting ready How to do it... See also Chapter 11: Implementing DataOps and DevOps on Databricks Technical requirements Using Databricks Repos to store code in Git Getting ready How to do it... There’s more... See also Automating tasks by using the Databricks CLI Getting ready How to do it... There’s more... See also Using the Databricks VSCode extension for local development and testing Getting ready How to do it... See also Using Databricks Asset Bundles (DABs) Getting ready How to do it... See also Leveraging GitHub Actions with Databricks Asset Bundles (DABs) Getting ready How to do it... See also Index About Packt Other Books You May Enjoy 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, youll 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