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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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1
Part 1 – Working with Apache Spark and Delta Lake
9
Part 2 – Data Engineering Capabilities within Databricks

Applying changes (CDC) to Delta tables with Delta Live Tables

Delta Live Tables also supports CDC, a technique that’s used to identify and capture changes made to data in a source database and then deliver those changes in real time to a target system. CDC enables you to keep your data lake or data warehouse in sync with your operational databases and also supports real-time analytics and data science.

One of the challenges of CDC is how to handle slowly changing dimensions (SCDs), which are dimensions that store and manage both current and historical data over time in a data warehouse. For example, a customer dimension may have attributes such as name, address, and phone number that can change over time. Depending on your business requirements, you may want to track the history of these changes in different ways. Several types of SCDs define how to handle these changes, such as Type 1 (overwrite), Type 2 (add new row), Type 3 (add new column), and so on.

In this recipe...

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