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

Ingesting data using KQL management commands

In the previous section, we imported our English Premier League data and you may have noticed that over half of the columns were related to betting statistics. In this section, we will create a custom CSV mapping schema and exclude those columns.

We will also introduce some KQL management commands. Like SQL, KQL has two categories of commands – data and management. The data commands allow us to query our data and the management commands allow us to manage our clusters, databases, tables, and schemas. We will cover KQL in depth in the next chapter, Introducing Kusto Query Language.

The first step is to create a table with the columns that we are interested in. When creating tables, we use the .create table command.

We will now specify our columns and their data types as shown in the following code snippet. Here, we are creating a table with clear column names and are not including any of the betting statistics. You may have...