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Azure Databricks Cookbook

You're reading from Azure Databricks Cookbook Accelerate and scale real-time analytics solutions using the Apache Spark-based analytics service

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781789809718

Length 452 pages

Edition 1st Edition

Languages

SQL

Tools

Azure

Concepts

Data Streaming

Authors (2):

Vinod Jaiswal

Phani Raj

View More author details

Table of Contents (12) Chapters

Preface

1. Chapter 1: Creating an Azure Databricks Service

2. Chapter 2: Reading and Writing Data from and to Various Azure Services and File Formats FREE CHAPTER

3. Chapter 3: Understanding Spark Query Execution

4. Chapter 4: Working with Streaming Data

5. Chapter 5: Integrating with Azure Key Vault, App Configuration, and Log Analytics

6. Chapter 6: Exploring Delta Lake in Azure Databricks

7. Chapter 7: Implementing Near-Real-Time Analytics and Building a Modern Data Warehouse

8. Chapter 8: Databricks SQL

9. Chapter 9: DevOps Integrations and Implementing CI/CD for Azure Databricks

10. Chapter 10: Understanding Security and Monitoring in Azure Databricks

11. Other Books You May Enjoy

Understanding the CI/CD process for Azure Databricks

In this recipe, we will learn what the advantage is of using the Continuous Integration and Continuous Delivery (CI/CD) process while working with Azure Databricks.

CI/CD is a method to frequently integrate code changes with a repository and deploy them to other environments. It offers an automated way of integrating, testing, and deploying code changes. CI in Azure Databricks enables developers to regularly build, test, and merge code changes to a shared repository. It's a solution for the very common problem of having multiple developers working on the same code or having multiple branches that could cause a conflict with each other in the software development life cycle.

CD refers to continuous delivery and/or continuous deployment. Continuous delivery means developers' changes are automatically tested and merged or uploaded to a repository such as Azure DevOps or Git. From there, changes can be deployed to other...

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Authors (2)

Raj

Raj

Rahul Raj has more than 7 years of IT industry experience in software development, business analysis, client communication, and consulting on medium-/large-scale projects in multiple domains. Currently, he works as a lead software engineer in a top software development firm. He has extensive experience in development activities comprising requirement analysis, design, coding, implementation, code review, testing, user training, and enhancements. He has written a number of articles about neural networks in Java and they are featured by DL4J/ official Java community channels. He is also a certified machine learning professional, certified by Vskills, the largest government certification body in India.

See other products by Raj

Jaiswal

Jaiswal

Vinod Jaiswal is an experienced data engineer, excels in transforming raw data into valuable insights. With over 8 years in Databricks, he designs and implements data pipelines, optimizes workflows, and crafts scalable solutions for intricate data challenges. Collaborating seamlessly with diverse teams, Vinod empowers them with tools and expertise to leverage data effectively. His dedication to staying updated on the latest data engineering trends ensures cutting-edge, robust solutions. Apart from technical prowess, Vinod is a proficient educator. Through presentations and mentoring, he shares his expertise, enabling others to harness the power of data within the Databricks ecosystem.

See other products by Jaiswal

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