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  • Book Overview & Buying Azure Databricks Cookbook
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Azure Databricks Cookbook

Azure Databricks Cookbook

By : Raj, Jaiswal
4.4 (28)
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Azure Databricks Cookbook

Azure Databricks Cookbook

4.4 (28)
By: Raj, 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)
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Chapter 3: Understanding Spark Query Execution

To write efficient Spark applications, we need to have some understanding of how Spark executes queries. Having a good understanding of how Spark executes a given query helps big data developers/engineers work efficiently with large volumes of data.

Query execution is a very broad subject, and, in this chapter, we will start by understanding jobs, stages, and tasks. Then, we will learn how Spark lazy evaluation works. Following this, we will learn how to check and understand the execution plan when working with DataFrames or SparkSQL. Later, we will learn how joins work in Spark and the different types of join algorithms Spark uses while joining two tables. Finally, we will learn about the input, output, and shuffle partitions and the storage benefits of using different file formats.

Knowing about the internals will help you troubleshoot and debug your Spark applications more efficiently. By the end of this chapter, you will know...

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Azure Databricks Cookbook
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