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)

Creating a Log Analytics workspace

As more and more Azure services are being used to build enterprise solutions, there needs to be a centralized location where we can collect performance and application metrics for various Azure services. This will help us understand how the service is functioning. Every Azure resource has a set of resource logs that provides information about the operations that are performed on the Azure service, as well as the health of that service. With the help of Azure Monitor Logs, we can collect data from resource logs, as well as performance metrics from applications and virtual machines, into a common Log Analytics workspace. We can also use these metrics to identify any specific trends, understand the performance of the service, or even find any anomalies. We can analyze the data that has been captured in a Log Analytics Workspace using Log Query, which was written in Kusto Query Language (KQL), and perform various types of Data Analytics operations. In...