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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

Using temporary datasets to validate data processing

As we’ll see in this section, creating a view is an effective method for validating the uniqueness of a primary key column. Additionally, we can also define alerts in the Databricks Data Intelligence Platform to notify the data stewards of potential data quality issues so that they can take appropriate measures to correct the data integrity.

We can leverage a view to validate the uniqueness of the primary key column. Recall the rides and drivers tables we defined in the previous section. In this example, we’re going to define a view on the incoming data to ensure the uniqueness of a primary key column across the rides Delta table. Create a new query in Databricks by navigating back to your workspace and right-clicking to open a dialog box. Select New | Query to open a new query in the editor. Next, rename the query with a meaningful name, such as rides_pk_validation_vw. Finally, add the following query text to the...

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Building Modern Data Applications Using Databricks Lakehouse
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