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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
9
Part 2 – Data Engineering Capabilities within Databricks

Tagging, commenting, and capturing metadata about data and AI assets using Databricks Unity Catalog

Tagging and documenting data and AI assets are important features of Unity Catalog which helps help you to do the following:

  • Improve data discovery: You can use tags and descriptions to annotate your data and AI assets with meaningful and consistent metadata, such as business terms, data quality, data sensitivity, and data ownership. This makes it easier for data consumers to find the data they need using the Unity Catalog search interface or SQL queries.
  • Improve data quality: You can use tags and descriptions to document the data lineage, provenance, and transformations of your data and AI assets. This helps you track the origin and history of your data, as well as the dependencies and impacts of any changes.
  • Improve data compliance: You can use tags and descriptions to classify your data and AI assets according to various regulatory and organizational policies, such...

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