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

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...