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)

Chapter 6: Exploring Delta Lake in Azure Databricks

Delta Lake is an open source storage layer that sits on a cloud object store such as Azure Data Lake Storage. Recently, many organizations have opted for data lake for their big data solution as they can keep all the data (batch and streaming data) in one location and later use it for various analytics and machine learning work. The main challenges with a data lake is that there is no schema enforcement, it does not guarantee consistency of the data, and does not provide Atomicity, Consistency, Isolation, and Durability (ACID) transactions. To overcome these challenges, Databricks has introduced Delta Lake. Delta Lake provides a lot of key benefits including ACID transactions, schema enforcements, schema evolution, update/delete operations on the data in the Delta Lake, and metadata handling.

By the end of this chapter, you will get to know how you can use Delta Lake and adopt Lakehouse patterns in your big data projects.

In...