How is Event Stream Processing leveraged?
Event stream processing (ESP) is the practice of processing a continuous flow of data points, or events, generated by a system. ESP enables various actions on these events, including aggregation (e.g., calculating sums, averages, and standard deviations), analytics (e.g., predicting future events based on historical patterns), transformation (e.g., converting data formats), enrichment (e.g., combining data with external sources for context), and ingestion (e.g., storing data in a database).
Event stream processing is often viewed as complementary to batch processing. Batch processing involves taking action on a large, static dataset, while event stream processing focuses on taking action on a continuous flow of data. Event stream processing is essential for scenarios where immediate action is required, making it ideal for real-time processing environments.
How does Event Stream Processing work?
Event stream processing (ESP) handles data one point at a time, focusing on the continuous flow of data rather than static datasets. This requires specialized technology.
Event stream processing environments rely on two key components: 1) event storage systems and 2) stream processing engines. Event storage systems, such as Apache Kafka, store time-stamped events. Stream processing engines, on the other hand, process these events in real-time, enabling actions like aggregation, filtering, and transformation. In-memory stream processors excel at handling high-volume, low-latency data streams.
ESP with AI Tools
It's common to see ESP and AI/advanced analytics deployed separately. While ESP excels at handling real-time data flows and AI at complex pattern recognition, their true potential is unleashed when combined. Together ESP and AI offer an effective solution for a growing array of business challenges.
Early in 2025, Roy Schulte of Artificial Intelligence & Complex Event Processing wrote:
“ESP and AI software tools are complementary in three ways:
- AI tools can make operational ESP business applications smarter at run time by applying a wide variety of advanced mathematical techniques, and more recently it includes Generative (Gen) AI transformer models.
- AI-based copilots can make it faster and easier for software engineers to develop and test ESP applications, regardless of whether the applications use AI or other analytical techniques at run time.
- Conversely, ESP can be used to implement streaming data engineering pipelines that prepare event streams for use by engineers as they design, build, and train AI and other analytical solutions.”