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Azure Databricks Cookbook

You're reading from Azure Databricks Cookbook

Product type Book

Published in Sep 2021

Publisher Packt

ISBN-13 9781789809718

Pages 452 pages

Edition 1st Edition

Languages

Concepts

Authors (2):

Phani Raj

Vinod Jaiswal

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

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

Handling concurrency

One of the most common problems in big data systems is that they are not compliant with ACID properties, or to put it more simply, let's say they only support some properties of ACID transactions between reads and writes, and even then, they still have a lot of limitations. But with Delta Lake, ACID compliance is possible, and you can now leverage the features of ACID transactions that you used to get in Relational Database Management Systems (RDBMSes). Delta Lake uses optimistic concurrency control for handling transactions.

Optimistic concurrency control provides a mechanism to handle concurrent transactions that are changing data in the table. It ensures that all the transactions are completed successfully. A Delta Lake write operation is performed in three stages:

Read – The latest version of the table in which the file needs to be modified is read.
Write – New data files are written.
Validate and commit – Validate...

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Authors (2)

Phani Raj

Phani Raj is an experienced data architect and a product manager having 15 years of experience working with customers on building data platforms on both on-prem and on cloud. Worked on designing and implementing large scale big data solutions for customers on different verticals. His passion for continuous learning and adapting to the dynamic nature of technology underscores his role as a trusted advisor in the realm of data architecture ,data science and product management.

See other products by Phani Raj

Vinod 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 Vinod Jaiswal

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