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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

Summary

In this chapter, we covered several techniques for implementing pipeline and data quality observability so that data teams can react as soon as problems arise and thwart major downstream disruptions. One of the major keys to becoming a successful data team is being able to react to issues quickly. We saw how alert notifications are built into many aspects of the Databricks Data Intelligence Platform and how we can configure different types of alert destinations to send notifications when conditions are not met.

We covered monitoring capabilities built into the Databricks platform, such as the pipeline event log that makes it easy for pipeline owners to query the data pipeline’s health, auditability, and performance, as well as data quality, in real time. We also saw how Lakehouse Monitoring is a robust and versatile feature that allows data teams to automatically monitor the statistical metrics of datasets and notify team members when thresholds have been crossed...

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Building Modern Data Applications Using Databricks Lakehouse
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