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

This chapter covered a lot of topics to ingest… I mean digest – pardon the pun. We started by learning about data ingestion in general, discussing the different types of data, such as structured, semi-structured, and unstructured. We then looked at the data management service in more detail to understand its role with regard to data ingestion. We also looked at the difference between batching and streaming data. We introduced the main ingestion categories: SDKs, managed pipelines such as Azure Event Grid, connections and plugins, and tools such as Azure Data Factory and one-click ingestion.

Then we learned about schema mapping, how they map external data to the columns in our ADX tables, and how to write our own schema maps for both CSV and JSON data. We created two schema maps for the English Premier League football results data, one for the CSV-based data and one for the JSON data, where we excluded the betting information and kept the actual football match...