Uses for event sourcing
Although early thoughts on event sourcing focused on the ability to re-create the current state of an entity, many modern implementations use event sourcing for additional purposes, including:
Re-creating user session activities in a distributed event-driven system
Many applications capture user interactions in timeboxed sessions. A session usually starts at the point of a user signing into the application and stays active until they sign out, or the session expires.
Event sourcing is valuable here to help users resume from where they left off or resolve any queries or disputes, as the system can chart each user’s journey.
Enabling audit tracing in situations where you cannot fully utilize logs
While many applications rely on accumulated, centrally stored logs to trace details of system behaviors, customer activities, financial data flows, etc., enter‐ prises need to comply with data privacy policies that prevent them from sending sensitive data and PII to the logs. With event sourcing, as the data resides inside the guarded cloud accounts, teams can build tools to reconstruct the flows from the event store.
Performing data analysis to gain insights
Data is a key driver behind many decisions in the modern digital business world. Event sourcing enables deeper insights and analytics at a fine-grained level. For example, the event store of a holiday booking system harvests every business event from several microservices that coordinate to help customers book their vacations. Often customers will spend time browsing through several destinations, offers, and customizable options, among other things, before com‐ pleting the booking or, in some cases, abandoning it. The events that occur during this process carry clues that can be used, for example, to identify popular (and unpopular) destinations, packages, and offers.
Since the conception of event sourcing a couple of decades ago, due to the emergence of the cloud and managed services, there have been vast changes in the volume of data captured and the available ingestion mechanisms and storage options. The data models of many (but not all) modern applications accommodate storing the change history for a certain period alongside the actual data, as per the business requirements, to enable quickly tracing all the activities.