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Scalable Data Analytics with Azure Data Explorer

You're reading from  Scalable Data Analytics with Azure Data Explorer

Product type Book
Published in Mar 2022
Publisher Packt
ISBN-13 9781801078542
Pages 364 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Jason Myerscough Jason Myerscough
Profile icon Jason Myerscough

Table of Contents (18) Chapters

Preface 1. Section 1: Introduction to Azure Data Explorer
2. Chapter 1: Introducing Azure Data Explorer 3. Chapter 2: Building Your Azure Data Explorer Environment 4. Chapter 3: Exploring the Azure Data Explorer UI 5. Section 2: Querying and Visualizing Your Data
6. Chapter 4: Ingesting Data in Azure Data Explorer 7. Chapter 5: Introducing the Kusto Query Language 8. Chapter 6: Introducing Time Series Analysis 9. Chapter 7: Identifying Patterns, Anomalies, and Trends in your Data 10. Chapter 8: Data Visualization with Azure Data Explorer and Power BI 11. Section 3: Advanced Azure Data Explorer Topics
12. Chapter 9: Monitoring and Troubleshooting Azure Data Explorer 13. Chapter 10: Azure Data Explorer Security 14. Chapter 11: Performance Tuning in Azure Data Explorer 15. Chapter 12: Cost Management in Azure Data Explorer 16. Chapter 13: Assessment 17. Other Books You May Enjoy

Technical requirements

The code examples for this chapter can be found in the Chapter04 folder of the repo: https://github.com/PacktPublishing/Scalable-Data-Analytics-with-Azure-Data-Explorer.git. The Chapter04 directory contains two directories, templates/, which contains our ARM templates, and datasets/, which contains our datasets that we will be ingesting.

One of the challenges when it comes to writing about data analytics is to have interesting datasets that are large enough to demonstrate the features of ADX and KQL. In this chapter, we will use the English Premier League's results to demonstrate how to ingest data in CSV and JSON format. A copy of the data is included in our repository and the original dataset can be found at https://datahub.io/sports-data/english-premier-league. The dataset provides Premier League results for the last 10 years.

Note

The infrastructure that we will deploy here will be reused later in the book. Feel free to either preserve the resources...

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