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


This chapter is one of the most important chapters in the book in terms of reusing the skills you have learned outside of ADX clusters. As mentioned, KQL is one of the fundamental keystones to Azure with regard to managing your logging and telemetry data. Data belonging to Auditing, Security Center, Application Insights, Monitoring, and Asset Management all reside in Log Analytic workspaces, which all use KQL for querying the data.

We learned what KQL is, where it can be used, and the basic syntax of KQL queries. We then learned about the basics of KQL, such as searching, filtering with where clauses, aggregations with summarize, formatting results, rendering graphs, and converting SQL statements to KQL using the EXPLAIN keyword.

Next, we learned about some of the most commonly used scalar functions and operators, such as data manipulation and formatting and string search using the has_cs and contains_cs operators. We also learned how to use the join operator to join...