Book Image

Scalable Data Analytics with Azure Data Explorer

By : Jason Myerscough
Book Image

Scalable Data Analytics with Azure Data Explorer

By: Jason Myerscough

Overview of this book

Azure Data Explorer (ADX) enables developers and data scientists to make data-driven business decisions. This book will help you rapidly explore and query your data at scale and secure your ADX clusters. The book begins by introducing you to ADX, its architecture, core features, and benefits. You'll learn how to securely deploy ADX instances and navigate through the ADX Web UI, cover data ingestion, and discover how to query and visualize your data using the powerful Kusto Query Language (KQL). Next, you'll get to grips with KQL operators and functions to efficiently query and explore your data, as well as perform time series analysis and search for anomalies and trends in your data. As you progress through the chapters, you'll explore advanced ADX topics, including deploying your ADX instances using Infrastructure as Code (IaC). The book also shows you how to manage your cluster performance and monthly ADX costs by handling cluster scaling and data retention periods. Finally, you'll understand how to secure your ADX environment by restricting access with best practices for improving your KQL query performance. By the end of this Azure book, you'll be able to securely deploy your own ADX instance, ingest data from multiple sources, rapidly query your data, and produce reports with KQL and Power BI.
Table of Contents (18 chapters)
Section 1: Introduction to Azure Data Explorer
Section 2: Querying and Visualizing Your Data
Section 3: Advanced Azure Data Explorer Topics

Chapter 5

  1. Write a query for our EnglishPremierLeague data and aggregate the number of matches refereed by each referee.


| summarize matches_refereed = count() by Referee
  1. What is the main difference between the search and where operators?

Answer: The search operator is simple and convenient to use, but you need to be careful with regard to the scope of the search. Searching across multiple tables and columns is an expensive operation and can cause performance issues.

  1. Aggregate all the event types in the StormEvents table for California and render the results as a column chart.


StormEvents | where State =~ "California"
 | summarize event=count() by EventType | render columnchart
  1. What type of join should you use if you want to include duplicate common column matches in the result set?

Answer: The inner join returns all matches.