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Scalable Data Analytics with Azure Data Explorer

Scalable Data Analytics with Azure Data Explorer

By : Jason Myerscough
4.8 (8)
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Scalable Data Analytics with Azure Data Explorer

Scalable Data Analytics with Azure Data Explorer

4.8 (8)
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)
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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 6: Introducing Time Series Analysis

In the previous chapter, we introduced Kusto Query Language (KQL) and learned how to search for and filter our data using the search and where operators. Although we only scratched the surface, we introduced some of the common string, numeric, date, and time operators that help us build complex search predicates. We also saw how we can render graphs to help visualize our result sets. We can accomplish a lot with these skills in that we can review our inventory in Azure Resource Graph and create monitoring alerts in Azure Monitor.

But what if we want to analyze our data, look at the historical and current patterns, and make forecasts for the future? This is where time series analysis can help. In this chapter, we will learn how to convert our data into a time series.

The goal of the chapter is to remain as practical as possible, but as always, an introduction and definition of time series analysis will help to reinforce our understanding...

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