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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

Data Governance with Unity Catalog

Databricks Unity Catalog is a unified governance solution for data and AI on a lakehouse. It allows you to securely discover, access, and collaborate on trusted data and AI assets from various platforms and clouds, leveraging AI to boost productivity and compliance.

Unity Catalog is a tool that helps you control, audit, trace, and discover data across Databricks workspaces. You can set up data access rules in one place and enforce them in all workspaces, following the ANSI SQL security standards. You can also see who created and used data assets, and how they did it, in any language. In addition, Unity Catalog allows you to label and describe data assets and offers a way to search for data that you need.

In this chapter, you will learn how to use Databricks Unity Catalog for data governance. We will cover the following recipes:

  • Connecting to cloud object storage using Unity Catalog
  • Creating and managing catalogs, schemas, volumes...

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