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

Description

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.

Who is this book 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.

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

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Databricks Certified Associate Developer for Apache Spark Using Python

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Amazon verified reviews

Vishnuvardhan Reddy Sep 16, 2024

Data Engineering with Databricks Cookbook" by Pulkit Chanda is an invaluable, hands-on guide for anyone aiming to build scalable and efficient data and AI solutions using Apache Spark on Databricks. This comprehensive resource is packed with practical recipes, offering step-by-step instructions to tackle common data engineering challenges. Whether you're a data engineer, architect, or developer working with large datasets, this book provides actionable insights and techniques to help you design and optimize robust data pipelines on Databricks. A must-read for professionals looking to elevate their data engineering skills!

Amazon Verified review

Amazon Customer Aug 05, 2024

Data engineering continues to expand into every data persona's required skillset, whether you know it or not. Data Scientists need to preprocess their data to serve their Machine Learning use cases, Data Analysts need to further clean their data for their Data Warehousing needs. This skillset will continue to become more and more valuable as more enterprises rely on their data to produce actionable insights.If you're looking for example code and reference frameworks for basic to intermediate data engineering tasks, this book is for you. The author does a great job balancing between open source tools (Apache Spark and Delta Lake) and more managed technologies (i.e. Spark on Databricks, Unity Catalog). It even goes through some CI/CD concepts and orchestration best practices which helps you take the basic skills you learn in the early chapters into production.Overall, I find myself coming back to this book any time I need to quickly ingest some new data or optimize my pipelines. I'd recommend this book to anyone looking to get a strong foundation in data engineering.

Satish Nadendla Aug 05, 2024

Pulkit Chadha has crafted "Data Engineering with Databricks Cookbook" with remarkable clarity and precision. The book includes comprehensive guides on implementing Delta Lake for ACID transactions, designing ETL pipelines with Apache Spark, optimizing Databricks clusters for performance, and managing real-time data streams using Structured Streaming. It breaks down complex data engineering tasks into easily digestible recipes, making advanced topics accessible even for those new to the field.Chadha’s articulation of complex concepts in a concise and understandable manner makes this book incredibly user-friendly. From data ingestion to performance tuning, each chapter provides practical, step-by-step guidance. The integration of Apache Spark, Databricks, and Delta Lake is seamlessly explained, offering best practices and optimization techniques that are immediately applicable. This book is an essential addition to any data engineer's library and a must-have for mastering the Databricks Lakehouse Platform.

Sivanagaraju Gadiparthi Jul 31, 2024

"Data Engineering with Databricks Cookbook" by Pulkit Chadha is an essential read for both novice and seasoned data engineers. The book offers a pragmatic approach to mastering the Databricks Lakehouse Platform through a series of well-structured recipes. Chadha’s extensive experience is evident in the practical insights and real-world examples that cover the full data engineering lifecycle—from data ingestion and transformation to data management and performance tuning. Each recipe is detailed, providing step-by-step instructions, code snippets, and explanations that make complex concepts accessible.Technically, the book excels with its thorough coverage of Apache Spark, Delta Lake, and Databricks. It addresses the intricacies of data ingestion, transformation, streaming, and performance optimization with clarity. The vocabulary is precise, avoiding unnecessary jargon, which makes the content approachable without diluting its technical depth. The book's organization allows readers to easily navigate through different sections based on their immediate needs.In summary, this cookbook is a valuable resource, offering a blend of foundational knowledge and advanced techniques. It stands out for its clarity, practical focus, and the depth of expertise shared by Chadha.

Jewell Jul 31, 2024

Consider this book a back-to-basics. It covers all of your data engineering fundamentals while walking you through the best approach to building systems on Databricks. You'll be able to build and solidify your data engineering foundation with this cookbook. I highly recommend this book to anyone new to the Databricks platform or someone who needs a quick refresher on best practices.

Data Engineering with Databricks Cookbook: Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake

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