Azure Data Explorer vs Prometheus

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 Prometheus 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 Prometheus 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.

Prometheus Overview

Prometheus is an open-source monitoring and alerting toolkit initially developed at SoundCloud in 2012. It has since become a widely adopted monitoring solution and a part of the Cloud Native Computing Foundation (CNCF) project. Prometheus focuses on providing real-time insights and alerts for containerized and microservices-based environments. Its primary use case is monitoring infrastructure and applications, with an emphasis on reliability and scalability.

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.

Prometheus for Time Series Data

Prometheus is specifically designed for time series data, as its primary focus is on monitoring and alerting based on the state of infrastructure and applications. It uses a pull-based model, where the Prometheus server scrapes metrics from the target systems at regular intervals. This model is suitable for monitoring dynamic environments, as it allows for automatic discovery and monitoring of new instances. However, Prometheus is not intended as a general-purpose time series database and might not be the best choice for high cardinality or long-term data storage.

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.

Prometheus Key Concepts

• Metric: A numeric representation of a particular aspect of a system, such as CPU usage or memory consumption.
• Time Series: A collection of data points for a metric, indexed by timestamp.
• Label: A key-value pair that provides metadata and context for a metric, enabling more granular querying and aggregation.
• PromQL: Prometheus uses its own query language called PromQL (Prometheus Query Language) for querying time series data and generating alerts.

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.

Prometheus Architecture

Prometheus is a single-server, standalone monitoring system that uses a pull-based approach to collect metrics from target systems. It stores time series data in a custom, highly compressed, on-disk format, optimized for fast querying and low resource usage. The architecture of Prometheus is modular and extensible, with components like exporters, service discovery mechanisms, and integrations with other monitoring systems. As a non-distributed system, it lacks built-in clustering or horizontal scalability, but it supports federation, allowing multiple Prometheus servers to share and aggregate data.