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

Understanding data ingestion

Before learning how data ingestion works with ADX, let's revisit the different types of data:

  • Structured data: When we think of structured data, we think of relational databases that are made up of tables consisting of rows and columns. Each column has a data type such as an integer or string, and it sometimes includes additional constraints such as fixed-length strings and strings with specific formats such as a postcode.
  • Semi-structured: When we think of semi-structured data, we think of JSON and XML. They have a structure defined with tags, but the format is typically less rigid than relational databases.
  • Unstructured data: Unstructured data is data that has no constraints, such as SMS messages, text files, and emails, and social media such as status posts, messages, and images.

As shown in Figure 4.1, ADX supports four categories of services that enable data ingestion:

Figure 4.1 – Data analysis pipeline

Figure 4.1 – Data...