In today's complex and fast-paced business environments, enterprises are often faced with the challenge of managing large amounts of data and ensuring that it is accurate, consistent, and accessible. One of the key pain points that many enterprises face is the loss of accuracy in your downstream data - the data that is passed from one system to another, often through a series of transformations and manipulations.
As more and more enterprises adopt event-driven architectures, they are finding that their traditional databases are not well-suited to supporting these systems. In particular, many enterprises have lost control of their downstream data, and are struggling to maintain data quality and consistency.
(Image: Events in a stream)
There are several reasons why this may happen. One of the most common is the use of traditional databases that were not designed with modern event-driven architecture in mind. These databases often throw away information when data is updated or deleted, which means that critical data and its context will be lost. This can lead to data quality issues and make it difficult to trace data back to its origin, which can have serious implications for the accuracy and reliability of downstream systems.
These databases were not designed to support distributed systems. An ordered log of changes is a key simplifier when building these systems, however, last-gen databases do not provide this type of ordering, which can make it difficult to coordinate services and maintain fault tolerance.
Enterprises that have adopted event-driven architectures without their underlying systems being event-oriented at the source (events on the inside, not just the outside) have discovered that they have lost traceability back to the events they rely on downstream systems and data. This can make it challenging to reconcile downstream data with its origins and can lead to data drift that cannot be easily corrected. These systems can facilitate near real-time communication between systems, but the promise of easier data analysis & quality is not fulfilled.
In an effort to make up for the lack of historical data in their operational systems, many enterprises have turned to data lakes and ETL pipelines to store and process data. However, these systems can be complex and difficult to manage, which can lead to data inconsistencies, incorrect data insights, and huge efforts in data cleansing.
One of the key solutions to these problems is the use of event sourcing and a dedicated stream database, like EventStoreDB. Event sourcing is a way of storing and managing data that involves storing a sequence of events that describe how the data has changed over time. This allows for more accurate and detailed tracking of data and makes it possible to recreate the state of the data at any point in time.
(Image: Example of events in EventStoreDB)
EventStoreDB is the database built to meet these challenges head-on. The database built for event sourcing it provides efficient storage of events, support for ACID transactions, and the ability to replay events to recreate the state of the data. Making it possible to build more flexible, scalable, and resilient applications that can support distributed systems, real-time data, and event-driven architectures.
Wanting to solve drift in your downstream data? Register today for our industry-leading training courses to learn more about Event Sourcing.