We’re excited to announce that Axual, provider of an all-in-one Kafka platform for enterprises, is listed as a representative vendor in the 2022 Gartner market guide for event stream processing.
We’re excited to announce that Axual, provider of an all-in-one Kafka platform for enterprises, is listed as a representative vendor in Gartner’s latest market guide for event stream processing.
Jeroen van Disseldorp, CEO & Founder of Axual, says, “We are proud to be mentioned three times in Gartner’s latest market guide. Since our inception in 2015, Axual has always tried to be on the forefront of streaming technology. To us, this is a great recognition that our technology and unique approach in enterprise-wide event streaming is well received and assessed by the market-leading analysts.”
As more and more data is available, event stream processing (ESP) platforms play an important role in turning that data into insights and inform real-time decisions. Gartner describes two common ESP usage scenarios:
There are a couple of trends that are influencing the adoption of ESP platforms.
The Internet of Things (IoT)
With an increasing number of data-generating sensors and meters being embedded in all manner of smart devices, there is a clear, growing need to harness and analyze IoT data. More and more organizations are adopting ESP platforms to power their data analytics with real-time IoT data. Use cases here are for example forecasting energy production and demand, trading efficiently on the energy markets, and to predict and get insights into energy usage.
Open source
Open source technologies such as Apache Flink, Apache Spark Streaming and Apache Kafka make event streaming more widely available. Gartner states, “these products are used mostly by organizations new to ESP or that prefer to do most of the software engineering themselves.”
The cloud
The rise of the cloud makes ESP platforms also more interesting for organizations with less budget and resources. Gartner states, “cloud ESP also brings the generic benefits of cloud: elastic scalability, self-provisioning or serverless operation, continuous patching and versioning, and offloading much of the work required for high availability and disaster recovery.”
Talking to our customers and prospects over the last six years we know that Apache Kafka is the most popular choice among architects and developers for event streaming. While Kafka is great, it’s also complex and quite a challenge to set up for secure and compliant event streaming at enterprise scale. That’s where Axual comes in.
Axual offers a complete streaming platform based on open source Kafka, extending it with self-service capabilities with which teams can easily setup and manage data streams through an intuitive, graphical interface. Axual allows enterprises to run their Kafka instance reliably where they need it. On-premise, in the cloud or in a hybrid setup.
Rabobank, one of our customers in the financial services industry, chose Apache Kafka as the engine for their event streaming platform, but quickly discovered that Kafka was missing a number of features to scale across the enterprise while guarding governance and security. This insight sparked the collaboration between Rabobank and Axual. Today, Axual is the digital nervous system at Rabobank facilitating 150+ of DevOps teams and millions of messages per day.
Interested in learning more about event streaming and Axual? Here are some great resources to get started:
Understanding Kafka can seem challenging, but in this blog, we simplify the concepts of Kafka’s maximum message size, how to use Kafka producers, and what consumer groups do. Ideal for beginners and those looking to expand their knowledge.
Rachel van Egmond 
Logius, with CGI and Axual, modernizes Dutch government communication using a scalable Kafka platform for efficient, secure, and future-proof digital services, streamlining interactions between government, citizens, and businesses.
Rachel van Egmond 
Linger.ms in Kafka optimizes batch sending delays, balancing throughput and latency. Kafka Operators help manage this setting in Kubernetes, simplifying configuration and performance tuning for efficient data handling.
Rachel van Egmond 
