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Data Engineering with Databricks Cookbook

Data Engineering with Databricks Cookbook

By : Pulkit Chadha
4.4 (7)
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Data Engineering with Databricks Cookbook

Data Engineering with Databricks Cookbook

4.4 (7)
By: Pulkit Chadha

Overview of this book

Written by a Senior Solutions Architect at Databricks, Data Engineering with Databricks Cookbook will show you how to effectively use Apache Spark, Delta Lake, and Databricks for data engineering, starting with comprehensive introduction to data ingestion and loading with Apache Spark. What makes this book unique is its recipe-based approach, which will help you put your knowledge to use straight away and tackle common problems. You’ll be introduced to various data manipulation and data transformation solutions that can be applied to data, find out how to manage and optimize Delta tables, and get to grips with ingesting and processing streaming data. The book will also show you how to improve the performance problems of Apache Spark apps and Delta Lake. Advanced recipes later in the book will teach you how to use Databricks to implement DataOps and DevOps practices, as well as how to orchestrate and schedule data pipelines using Databricks Workflows. You’ll also go through the full process of setup and configuration of the Unity Catalog for data governance. By the end of this book, you’ll be well-versed in building reliable and scalable data pipelines using modern data engineering technologies.
Table of Contents (16 chapters)
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1
Part 1 – Working with Apache Spark and Delta Lake
9
Part 2 – Data Engineering Capabilities within Databricks

Leveraging GitHub Actions with Databricks Asset Bundles (DABs)

CI/CD are software development practices that aim to automate the building, testing, and deployment of code on various platforms. CI/CD can help developers deliver high-quality software faster and more reliably, as well as reduce the risks and costs associated with manual and error-prone processes. Using CI/CD with Databricks can enable developers to leverage the power and flexibility of the platform while ensuring that their code is consistent, tested, and deployable across different environments and teams. CI/CD can also help developers integrate their Databricks workflows with other tools and services, such as source control, issue tracking, monitoring, and security. By using DABs, you can leverage popular CI/CD tools, such as GitHub Actions, Azure DevOps, Jenkins, and CircleCI, to automate the testing and deployment of your bundle projects. This way, you can reduce errors, promote software best practices, and iterate...

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