Azure Data Explorer vs SQL Server

Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Azure Data Explorer and SQL Server so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Azure Data Explorer and SQL Server perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

Azure Data Explorer Overview

Azure Data Explorer is a cloud-based, fully managed, big data analytics platform offered as part of the Microsoft Azure platform. It was announced by Microsoft in 2018 and is available as a PaaS offering. Azure Data Explorer provides high-performance capabilities for ingesting and querying telemetry, logs, and time series data.

SQL Server Overview

Microsoft SQL Server is a powerful and widely used relational database management system developed by Microsoft. Initially released in 1989, it has evolved over the years to become one of the most popular database systems for businesses of all sizes. SQL Server is known for its robust performance, security, and ease of use. It supports a variety of platforms, including Windows, Linux, and containers, providing flexibility for different deployment scenarios.

Azure Data Explorer for Time Series Data

Azure Data Explorer is well-suited for handling time series data. Its high-performance capabilities and ability to ingest large volumes of data make it suitable for analyzing and querying time series data in near real-time. With its advanced query operators, such as calculated columns, searching and filtering on rows, group by-aggregates, and joins, Azure Data Explorer enables efficient analysis of time series data. Its scalable architecture and distributed nature ensure that it can handle the velocity and volume requirements of time series data effectively.

SQL Server for Time Series Data

While Microsoft SQL Server is primarily a relational database, it does offer support for time series data through various features and optimizations. Temporal tables allow for tracking changes in data over time, providing an efficient way to store and query historical data. Indexing and partitioning can be leveraged to optimize time series data storage and retrieval. However, SQL Server may not be the best choice for applications requiring high write or query throughput specifically for time series data, as specialized time series databases offer more optimized solutions as well as a variety of developer productivity features that speed up development time for applications that heavily use time series data.

Azure Data Explorer Key Concepts

• Relational Data Model: Azure Data Explorer is a distributed database based on relational database management systems. It supports entities such as databases, tables, functions, and columns. Unlike traditional RDBMS, Azure Data Explorer does not enforce constraints like key uniqueness, primary keys, or foreign keys. Instead, the necessary relationships are established at query time.
• Kusto Query Language (KQL): Azure Data Explorer uses KQL, a powerful and expressive query language, to enable users to explore and analyze their data with ease.
• Extents: In Azure Data Explorer, data is organized into units called extents, which are immutable, compressed sets of records that can be efficiently stored and queried.

SQL Server Key Concepts

• T-SQL: Transact-SQL, an extension of SQL that adds procedural programming elements, such as loops, conditional statements, and error handling, to the standard SQL language.
• SSMS: SQL Server Management Studio, an integrated environment for managing SQL Server instances, databases, and objects.
• Always On: A suite of high availability and disaster recovery features in SQL Server, including Always On Availability Groups and Always On Failover Cluster Instances.

Azure Data Explorer Architecture

Azure Data Explorer is built on a cloud-native, distributed architecture that supports both NoSQL and SQL-like querying capabilities. It is a columnar storage-based database that leverages compressed, immutable data extents for efficient storage and retrieval. The core components of Azure Data Explorer’s architecture include the Control Plane, Data Management, and Query Processing. The Control Plane is responsible for managing resources and metadata, while the Data Management component handles data ingestion and organization. Query Processing is responsible for executing queries and returning results to users.

SQL Server Architecture

Microsoft SQL Server is a relational database that uses SQL for querying and manipulating data. It follows a client-server architecture, with the database server hosting the data and processing requests from clients. SQL Server supports both on-premises and cloud-based deployment through Azure SQL Database, a managed service offering in the Microsoft Azure cloud. SQL Server’s architecture includes components such as the Database Engine, which processes data storage and retrieval, and various services for reporting, integration, and analysis.