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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

Introducing DLT concepts

The DLT framework automatically manages task orchestration, cluster creation, and exception handling, allowing data engineers to focus on defining transformations, data enrichment, and data validation logic. Data engineers will define a data pipeline using one or more dataset types. Under the hood, the DLT system will determine how to keep these datasets up to date. A data pipeline using the DLT framework is made up of the streaming tables, materialized views, and views dataset types, which we’ll discuss in detail in the following sections. We’ll also briefly discuss how to visualize the pipeline, view its triggering method, and look at the entire pipeline data flow from a bird’s-eye view. We’ll also briefly understand the different types of Databricks compute and runtime, and Unity Catalog. Let’s go ahead and get started.

Streaming tables

Streaming tables leverage the benefits of Delta Lake and Spark Structured Streaming...

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Building Modern Data Applications Using Databricks Lakehouse
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