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

Trend analysis with KQL

As we discussed in Chapter 6, Introducing Time Series Analysis, one of the components of time series data is a trend. A trend helps visualize and predict the long-term direction of data. The trend is either positive, also known as an upward trend, or negative, also known as a downward trend. KQL provides two functions, series_fit_line() and series_fit_2lines(), for calculating the trend. We will begin by looking at series_fit_line() before looking at series_fit_2lines().

Applying linear regression with KQL

The series_fit_line() function performs linear regression to calculate the best fit line, also known as the regression line, for our original time series. Once we have calculated our regression line, we can identify the positive and negative relationships between our x-axis, also known as the independent variable, and our y-axis, also known as the dependent variable. The series_fit_line() function takes one argument, which is a time series, and returns...