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Data Engineering with Databricks Cookbook

Data Engineering with Databricks Cookbook

By : Pulkit Chadha
4.4 (7)
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Data Engineering with Databricks Cookbook

Data Engineering with Databricks Cookbook

4.4 (7)
By: Pulkit Chadha

Overview of this book

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.
Table of Contents (16 chapters)
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Part 1 – Working with Apache Spark and Delta Lake
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Part 2 – Data Engineering Capabilities within Databricks

Part 1 – Working with Apache Spark and Delta Lake

In this part, we will explore the essentials of data operations with Apache Spark and Delta Lake, covering data ingestion, extraction, transformation, and manipulation to align with business analytics. We will delve into Delta Lake for reliable data management with ACID transactions and versioning, and tackle streaming data ingestion and processing for real-time insights. This part concludes with performance tuning strategies for both Apache Spark and Delta Lake, ensuring efficient data processing within the Lakehouse architecture.

This part contains the following chapters:

  • Chapter 1, Data Ingestion and Data Extraction with Apache Spark
  • Chapter 2, Data Transformation and Data Manipulation with Apache Spark
  • Chapter 3, Data Management with Delta Lake
  • Chapter 4, Ingesting Streaming Data
  • Chapter 5, Processing Streaming Data
  • Chapter 6, Performance Tuning with Apache Spark
  • Chapter 7, Performance Tuning in Delta Lake
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