Table of Contents
- Executive Summary
- Comparison Background and Setup
- Platforms Tested and Compared
- Comparison Findings
- Total Cost of Ownership
- Benefits and Capabilities
- Appendix: In-Depth TCO Comparison Breakdown
- About DataStax
- About William McKnight
- About Jake Dolezal
- About GigaOm
Competitive markets demand rapid, well-informed decision-making to succeed. In response, enterprises are building fast and scalable data infrastructures to fuel time-sensitive decisions, provide rich customer experiences enable better business efficiencies, and gain a competitive edge.
There are numerous applications being developed that make autonomous decisions about how data is produced, consumed, analyzed, and reacted to in real time. However, if data is not captured within a specific timeframe, its value is lost, and the decision or action that needs to take place never occurs or happens too late.
Fortunately, there are technologies designed to handle large volumes of time-sensitive streaming data. Known by names like streaming, messaging, live feeds, real-time, and event-driven, this category of data needs special attention because delayed processing and decision-making can negatively affect its value. A sudden price change, a critical threshold met, an anomaly detected, a sensor reading changing rapidly, an outlier in a log file—any of these can be of immense value to a decision-maker or a process but only if alerted in time to affect the outcome.
This report’s focus is on real-time data and how autonomous systems can be fed this data at scale while producing reliable performance. To shed light on this challenge, we assess and benchmark two leading streaming data technologies—DataStax Astra Streaming and Apache ActiveMQ Artemis. Both solutions process massive amounts of streaming data from social media, logging systems, clickstreams, Internet-of-Things devices, and more. However, they differ in important ways from throughput and overall scalability to operational ease of use and cost, as we reveal in our hands-on testing.
Astra Streaming is a fully managed, cloud-native, streaming-as-a-service solution built on Apache Pulsar. As a managed solution, Astra Streaming eliminates the overhead of installing, operating, and scaling Pulsar. Astra Streaming also offers out-of-the box support and interoperability between Java Messaging Service (JMS), RabbitMQ, and Kafka in a single platform. This means if your existing applications are relying on these platforms, you can immediately convert them into streaming apps with little to no code changes.
Apache ActiveMQ is an open source, multiprotocol, Java-based message broker. It supports industry standard protocols across a broad range of languages and platforms. There are currently two “flavors” of ActiveMQ available—the well-known classic broker and the next-generation broker code-named Artemis, both compatible with JMS.
In our comparative study, we used the Starlight for JMS feature included in DataStax Astra Streaming along with self-managed open-source Apache ActiveMQ Artemis JMS instances. We found several notable differences and benefits for modernizing a JMS-based data streaming stack.
Astra Streaming with Starlight for JMS architecture is consolidated and simplified, and as a fully managed platform, it provides a number of benefits, including platform management, administration, and recovery functions.
The performance and resiliency of Astra Streaming can easily match ActiveMQ Artemis without the burden of scaling out infrastructure (or scaling down when demand is light). You simply pay for what you use, and DataStax manages the operational back end for you.
We found that in situations where message-per-second throughput rapidly and frequently varies (bursting), Astra Streaming was 2x cheaper in infrastructure costs and up to 4x cheaper in total cost of ownership.
Modernizing JMS-based applications to fully managed Astra Streaming would have many benefits and capability enhancements, including real-time data integration, analytics, and AI/ML applications.