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

Azure has changed the way data applications are designed and implemented and how data is processed and stored. As we see more data coming from various disparate sources, we need to have better tools and techniques to handle streamed, batched, semi-structured, unstructured, and relational data together. Modern data solutions are being built in such a way that they define a framework that describes how data can be read from various sources, processed together, and stored or sent to other streaming consumers to generate meaningful insights from the raw data.

In this chapter, we will learn how to ingest data coming from disparate sources such as Azure Event Hubs, Azure Data Lake Storage Gen2 (ADLS Gen2) storage, and Azure SQL Database, and how this data can be processed together and stored as a data warehouse model with Facts and Dimension Azure Synapse Analytics, and store processed and raw data...