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

Summary

Congratulations on completing this chapter! In this chapter, we started by introducing time series and time series analysis. You learned what a time series is and the properties of a time series, such as trends, seasonality, variations, and cycles.

Next, we dived into the practical aspects of time series and learned how to create them using the make-series operator. You learned that the make-series operator returns the series as arrays and that the time chart can render this data without any additional information.

We then worked through a couple of examples and generated a time series. We created a time series in the help cluster and generated a time chart to visualize the number of requests per country. We then learned about the Log Analytics demo workspace and generated a time series to understand how many security patches had been applied in the last 100 days.

Finally, we looked at the series_stats() function, which calculates statistics for our time series data...