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

Troubleshooting ADX

As you may recall from Chapter 4, Ingesting Data in Azure Data Explorer, we set up infrastructure to ingest data from a storage account using an event grid and an event hub. Since we did not configure diagnostics at the time, the only way to check whether the ingestion succeeded was to run a query to check whether any data was available. Depending on the ingestion policy, you had to wait up to 5 minutes for the data to be ingested. Now, imagine an error occurred—how would you know? Should you refresh your browser or continuously execute a query to return the number of rows? No! That does not scale and, like me, you probably have better things to do with your time than continuously hitting Shift + Enter to execute a query.

In this section, we will intentionally introduce an error with our data ingestion process, and then we will learn how to troubleshoot such issues by looking at ADX's metrics and diagnostic logs using Log Analytics.

Note

In...