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 window aggregation on streaming data

We often encounter situations where we don't want the streaming data to be processed as is and want to aggregate the data and then perform some more transformation before data is written to the destination. Spark provides us with an option for performing aggregation on data using a Windows function for both non-overlapping and overlapping windows. In this recipe, we will learn how to use aggregations using the window function on streaming data.

Getting ready

We will be using Event Hubs for Kafka as the source for streaming data.

You can use the Python script, https://github.com/PacktPublishing/Azure-Databricks-Cookbook/blob/main/Chapter04/PythonCode/KafkaEventHub_Windows.py, which will push the data to Event Hubs for Kafka as the streaming data producer. Change the topic name in the Python script to kafkaenabledhub1.

You can refer to the Reading data from Kafka-enabled Event Hubs recipe to understand how to get the...