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)

Storage benefits of different file types

Storage formats are a way to define how data is stored in a file. Hadoop doesn't have a default file format, but it supports multiple file formats for storing data. Some of the common storage formats for Hadoop are as follows:

  • Text files
  • Sequence files
  • Parquet files
  • Record-columnar (RC) files
  • Optimized row columnar (ORC) files
  • Avro files

Choosing a write file format will provide significant advantages, such as the following:

  • Optimized performance while reading and writing data
  • Schema evaluation support (allows us to change the attributes in a dataset)
  • Higher compression, resulting in less storage space being required
  • Splittable files (files can be read in parts)

Let's focus on columnar storage formats as they are widely used in big data applications because of how they store data and can be queried by the SQL engine. The columnar format is very useful when a subset of data...