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Building Modern Data Applications Using Databricks Lakehouse

Building Modern Data Applications Using Databricks Lakehouse

By : Will Girten
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Building Modern Data Applications Using Databricks Lakehouse

Building Modern Data Applications Using Databricks Lakehouse

5 (1)
By: Will Girten

Overview of this book

With so many tools to choose from in today’s data engineering development stack as well as operational complexity, this often overwhelms data engineers, causing them to spend less time gleaning value from their data and more time maintaining complex data pipelines. Guided by a lead specialist solutions architect at Databricks with 10+ years of experience in data and AI, this book shows you how the Delta Live Tables framework simplifies data pipeline development by allowing you to focus on defining input data sources, transformation logic, and output table destinations. This book gives you an overview of the Delta Lake format, the Databricks Data Intelligence Platform, and the Delta Live Tables framework. It teaches you how to apply data transformations by implementing the Databricks medallion architecture and continuously monitor the data quality of your pipelines. You’ll learn how to handle incoming data using the Databricks Auto Loader feature and automate real-time data processing using Databricks workflows. You’ll master how to recover from runtime errors automatically. By the end of this book, you’ll be able to build a real-time data pipeline from scratch using Delta Live Tables, leverage CI/CD tools to deploy data pipeline changes automatically across deployment environments, and monitor, control, and optimize cloud costs.
Table of Contents (16 chapters)
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1
Part 1:Near-Real-Time Data Pipelines for the Lakehouse
6
Part 2:Securing the Lakehouse Using the Unity Catalog
10
Part 3:Continuous Integration, Continuous Deployment, and Continuous Monitoring

Mastering Data Governance in the Lakehouse with Unity Catalog

In this chapter, we’ll dive into the implementation of effective data governance for the lakehouse using Unity Catalog. We’ll cover enabling Unity Catalog on an existing Databricks workspace, implementing data cataloging for data discovery, enforcing fine-grained data access at the table, row and column levels, as well as tracking data lineage. By the end of the chapter, you’ll be armed with industry best practices around data governance and will have gained real-world insights for enhancing data security and compliance.

In this chapter, we’re going to cover the following main topics:

  • Understanding data governance in the lakehouse
  • Enabling Unity Catalog on an existing Databricks workspace
  • Identity federation in Unity Catalog
  • Data discovery and cataloging
  • Hands-on lab – data masking healthcare datasets
CONTINUE READING
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Programming languages
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Building Modern Data Applications Using Databricks Lakehouse
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