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Event Stream Processing

Event Stream Processing is a method for performing real-time calculations for data in motion. InfinyOn Cloud offers a high performance event stream processing engine that can be used to stream, transfrom, and load data in real-time. Common inputs for event stream processing are:

  • Sensor data from machines or mobile devices
  • Customer data
  • Transaction data
  • Payment data
  • Geo location data

Benefits of Event Stream Processing

Event stream processing offers numerous advantages to organizations. The ability to analyze and respond to data in real-time, rather than waiting for it to be processed and analyzed later, enables organizations to make faster and more informed decisions. Additionally, event stream processing can handle large volumes of data in real-time, making it ideal for applications such as detecting financial fraud and streaming analytics. Don’t miss out on the benefits of real-time data processing.


What are some of the problems with Event Stream Processing?

Event stream processing poses many challenges for organizations. One of the main challenges is the complexity of the systems, which can make it difficult to design and maintain them. Additionally, event stream processing systems require significant computing resources, and may not be suitable for all types of data or applications. It also can be difficult to handle high data volumes, maintain low latency, and handle data that is in motion. These challenges can make it difficult for organizations to achieve their desired outcomes with event stream processing.


How do we solve this?

InfinyOn Cloud is a unified platform for event stream processing and real-time data tranformation. Built on Fluvio, an open-source software similar to Apache Kafka and written in Rust, it provides enterprises with a strategic advantage in terms of scale and security. Our Java vs. Rust comparison provides further information.

With InfinyOn Cloud, you can collect data from endpoints in any location with fast, single-digit millisecond latency. The platform makes it easy to spin up a cluster, select source and sink connectors from our catalog, configure producers and consumers, then create a topic for streaming data. SmartModules can be used to aggregate, filter, or map streaming data, and users can share and reuse data transformations on the SmartModule Hub to quickly build sophisticated data pipelines. InfinyOn Cloud streamlines the process of setting up, deploying, and managing your cluster. Upgrade your event stream processing and real-time data transformation capabilities with InfinyOn Cloud.


Event Stream Processing Use Cases

Event stream processing is commonly used for a variety of purposes such as: change data capture, data cleansing, data protection, ETL to STL, fraud detection, integrations management, log analytics, predictive maintenance, real-time inventory management, real-time payments, secure transactions, and supply chain automation.


Reference Architecture Diagram

MQTT to Postgres Reference Architecture Diagram

Learn more about event stream processing with InfinyOn Cloud. Click the button below to schedule a demo.