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

Scaling and cost management

One of the aspects that we have not discussed in detail so far is scaling. One of the design principles of cloud computing is elasticity, and Azure allows us to scale our resources on demand. Scaling in the context of elasticity comes in two dimensions. One is vertical scaling, which refers to increasing the specification of a VM or ADX engine node. For instance, changing the engine SKU from Standard_E64i_v3 to Standard_E80ids_v4 is an example of vertical scaling.

The second dimension is horizontal scaling. Horizontal scaling refers to adding more VMs or engines. For instance, increasing the number of engines is a form of horizontal scaling.

ADX can take care of scaling for us and this is referred to as auto-scaling, but there can be up to 10 minutes of downtime. If downtime is an issue, then you can also use manual scaling and decide when to scale your cluster—for instance, you could manually scale your cluster outside of peak hours.

If...