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)
1
Section 1: Introduction to Azure Data Explorer
5
Section 2: Querying and Visualizing Your Data
11
Section 3: Advanced Azure Data Explorer Topics

Introducing the basics of KQL

Throughout this chapter, I will draw comparisons between SQL and KQL to demonstrate similarities and showcase the simplicity of KQL. Before we start to look at the basic data transformation operators, let's first look at how to query a table in the simplest form.

In SQL, if you want to query a table and return all columns and rows, you can execute a query as follows:

Select * from StormEvents

The query returns all the rows and columns for StormEvents. You can even execute the SQL query in the ADX Web UI. The equivalent query in KQL is simply the table name:

StormEvents

As shown in Figure 5.2, the query returns all rows and columns (59,066 records) in approximately 23 seconds:

Figure 5.2 – The simplest KQL query

This type of query can be expensive in terms of performance since it returns all records and columns, and tables can contain millions of records. This type of query is normally used with limit...