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

Chapter 7: Identifying Patterns, Anomalies, and Trends in your Data

In the previous chapter, we introduced the concept and properties of time series and demonstrated how to create time series and render them as time charts in Kusto Query Language (KQL). Now that we are familiar with time series and their properties such as seasonality, variations, and trends, the next step is to learn how to identify these patterns and properties in our data.

The goal of the chapter is to remain as practical as possible and focus on learning how and when to use KQL's functions and operators, which allow us to analyze our data, identify trends and anomalies, and make forecasts so that we can gain better insights into our data.

In this chapter, we will begin by learning about moving averages and how moving averages can help reduce noise and smoothen our time series data. Next, we will learn how to perform trend analysis in KQL by using linear regression.

Finally, we will learn how to determine...