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

What is time series analysis?

With our data now ingested into ADX, we can write queries using KQL to filter and search for data using search predicates. These queries are sufficient when your data is clean, structured, and you know what you are looking for, but what if we would like to do a more sophisticated exploration of our data? How can we look at trends, detect anomalies and outliers, and make forecasts, which, in turn, can help us make better decisions? One solution is time series analysis.

Note

The primary goal of this chapter is to focus on the practical aspects of time series analysis using the features provided by KQL. Although we will introduce the basics of time series analysis, an in-depth discussion is outside the scope of this book. There is a lot of good literature regarding time series analysis at https://www.packtpub.com.

Let's begin by discussing what a time series is. A time series is a sequence of data points captured over time. CPU, RAM, and disk...