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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 a lot of topics surrounding the data quality of the data in our lakehouse. We learned how the integrity of a table can be enforced using NOT NULL and CHECK constraints in Delta Lake. We also defined relationships between the tables in our lakehouse using PRIMARY KEY and FOREIGN KEY constraints. Next, we saw how we could enforce primary key uniqueness across our Delta tables using views to validate the data in our tables. We also saw just how easy it was to update the behavior of our data pipeline when incoming rows violated data quality constraints, allowing data engineering teams to react to downstream processes that have the potential to break from poor-quality data. Finally, we saw a practical example of how we can use expectations to create a conditional data flow in our pipeline, allowing our data stewards to quarantine and correct data that doesn’t meet the expected data quality.

In the next chapter, we’re going to get into...

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Building Modern Data Applications Using Databricks Lakehouse
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