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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

  1. What are the properties of a time series?

Answer:

  • Trend: This refers to the long-term direction of the data. For example, the data can have a positive growth known as an upward trend, or it can have a negative growth known as a downward trend, or the data could also plateau.
  • Variations: This refers to the peaks and troughs in the data.
  • Seasonality: This refers to reoccurring patterns at regular intervals.
  • Cycles: These are like seasonality meaning there is a consistent pattern, but the patterns are not consistent at regular time intervals.
  1. What operator can we use to generate a time series?

Answer: The make-series operator.

  1. Can you fill in the blanks of this query to display the number of patches installed in the last 30 days and render the results as a time chart?
    let startTime = ago(____);
    let endTime = now();
    let binSize = 7d;
    Update
    | where Classification == "Security Updates"
    | make-series security_updates...
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Scalable Data Analytics with Azure Data Explorer
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