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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

Deploying Delta Live Tables pipelines with Databricks Asset Bundles

Databricks Asset Bundles is a new feature that allows you to deploy your data, analytics, and machine learning projects as a collection of source files. You can use a YAML file to specify the resources and settings for your project, such as jobs, pipelines, endpoints, experiments, and models. This way, you can easily manage your code and infrastructure in a consistent and automated way.

The following are some of the benefits of using Databricks Asset Bundles:

  • You can use best practice tools and processes to work with source code, such as source control, code review, testing, and CI/CD
  • You can streamline your local development with IDEs and run your resources before deploying them to production
  • You can configure your deployments across multiple workspaces, regions, and clouds

In this recipe, you will learn how to package your pipeline as a Databricks Access Bundle and deploy it to a production...

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