Aggregation involves combining multiple individual events into a single data point or a summarized result, making it easier to interpret and act upon. For example, in a financial system, aggregation could mean calculating the total sales for a day, rather than analyzing each individual sale. Data is often represented through measures such as sum, average, minimum, maximum, count, or percentiles.
Aggregation reduces the volume of data being processed or stored. Instead of processing every single event in its raw form, an aggregate value can be computed and sent forward for further analysis. For instance, rather than storing every individual temperature reading, an aggregation might only store the average temperature every hour.
This helps optimize system performance, storage, and data transmission, especially in real-time or large-scale event-driven architectures (like billing systems, IoT, or big data platforms).
Aggregation can be crucial in real-time event processing. By constantly updating aggregated values (e.g., a running total or average), systems can offer up-to-the-minute insights that drive immediate actions.
Aggregation can also improve the cost-efficiency of event processing systems by limiting the computational resources needed. Instead of processing raw events individually, systems aggregate data, reducing the complexity of computation and the number of operations performed.
So, you should choose the right event processing platform that has powerful event aggregation mechanisms built in! Next time, we’ll discuss why aggregation is not as easy as it sounds. 🙂