Book Image

Azure Databricks Cookbook

By : Phani Raj, Vinod Jaiswal
Book Image

Azure Databricks Cookbook

By: Phani Raj, Vinod Jaiswal

Overview of this book

Azure Databricks is a unified collaborative platform for performing scalable analytics in an interactive environment. The Azure Databricks Cookbook provides recipes to get hands-on with the analytics process, including ingesting data from various batch and streaming sources and building a modern data warehouse. The book starts by teaching you how to create an Azure Databricks instance within the Azure portal, Azure CLI, and ARM templates. You’ll work through clusters in Databricks and explore recipes for ingesting data from sources, including files, databases, and streaming sources such as Apache Kafka and EventHub. The book will help you explore all the features supported by Azure Databricks for building powerful end-to-end data pipelines. You'll also find out how to build a modern data warehouse by using Delta tables and Azure Synapse Analytics. Later, you’ll learn how to write ad hoc queries and extract meaningful insights from the data lake by creating visualizations and dashboards with Databricks SQL. Finally, you'll deploy and productionize a data pipeline as well as deploy notebooks and Azure Databricks service using continuous integration and continuous delivery (CI/CD). By the end of this Azure book, you'll be able to use Azure Databricks to streamline different processes involved in building data-driven apps.
Table of Contents (12 chapters)

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:

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