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)

Understanding trigger options

In this recipe, we will understand various trigger options that are available in Spark Structured Streaming and learn under which scenarios a specific type of trigger option can be used. The trigger option for a streaming query identifies how quickly streaming data needs to be processed. It defines whether the streaming query needs to be processed in micro-batch mode or continuously. The following are the different types of triggers that are available:

  • Default (when unspecified): New data is processed as soon as the current micro-batch completes. No interval is set in this option.
  • Fixed Interval – micro-batch: We define a processing time that controls how often the micro-batches are executed. This is preferred in many use cases.
  • One Time – micro-batch: This will execute as a micro-batch only once, process all the data that is available, and then stop. It can be used in scenarios where data arrives once every hour or so.
  • ...