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

  1. What is the purpose of moving averages?

Answer: Moving averages allow us to remove noise and smooth our data.

  1. What is the purpose of linear regression?

Answer: The purpose of linear regression is to identify trends – both positive and negative – in our time series.

  1. What are the extra steps required to render time charts in log analytics?

Answer: With log analytics, we must expand our time series using mv-expand and convert our values from dynamic data types to their specific data types. Then, we must project the columns we want to pipe to the render operator.

  1. In Figure 7.12, we rendered an anomaly chart to display the anomalies in the time series. Using series_fir(), generate a smoother graph without the anomalies. Once you generate a smoother output, pass your data to series_decompose_anomalies() to see if there are still any anomalies. The query to generate the graph in Figure 7.12 is as follows. You will need...