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  • Book Overview & Buying Building Modern Data Applications Using Databricks Lakehouse
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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

Monitoring Data Pipelines in Production

In the previous chapters, we learned how to build, configure, and deploy data pipelines using the Databricks Data Intelligence Platform. To round off managing data pipelines for the lakehouse, in this final chapter of the book, we’ll dive into the crucial task of monitoring data pipelines in production. We’ll learn how to leverage comprehensive monitoring techniques directly from the Databricks Data Intelligence Platform to track pipeline health, pipeline performance, and data quality, to name a few. We will also implement a few real-world examples through hands-on exercises. Lastly, we’ll look at the best practices for ensuring that your data pipelines run smoothly, enabling timely issue detection and resolution, and ensuring the delivery of reliable and accurate data for your analytics and business needs.

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

  • Introduction to data pipeline monitoring...
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Building Modern Data Applications Using Databricks Lakehouse
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