What is Event Streaming?
Event streaming is an architectural approach in data engineering that treats data not only as static records in a database, but as a continuous flow of discrete occurrences (events) moving through an organization’s systems as they happen.
In a traditional batch-oriented architecture, data is passive. If a customer adds an item to an online shopping cart, that action is written to a PostgreSQL database. It sits there until an ETL job runs at 3:00 AM the next morning to extract and analyze it. Event streaming changes this. When the customer adds the item to the cart, that action is captured as an “event.” It is published to a streaming platform (like Apache Kafka or Apache Pulsar), allowing multiple downstream systems to react within seconds or less.
The Anatomy of an Event
An event is different from a row in a relational database. It is a record of a specific action that occurred at a specific point in time.
A typical event (often encoded in JSON, Avro, or Protobuf) contains three main components:
- The Key: The identifier of the entity (e.g.,
Customer_ID: 1045). - The Value: The state change or action (e.g.,
Action: Added_To_Cart,Item: Nike_Shoes,Price: 120.00). - The Timestamp: When the event occurred.
Events are appended to the log rather than updated in place. Within the retention period, this gives an ordered audit trail of what happened in the business, and avoids the lost history that occurs when database records are overwritten. (Platforms like Kafka do remove events after a configured retention time or through log compaction, so long-term history is usually also written to a lakehouse.)
The Nervous System of the Enterprise
Event streaming platforms often act as the central nervous system connecting decoupled microservices and the modern Data Lakehouse.
Decoupling Microservices
In a tightly coupled design, the checkout service calls the inventory service directly to update stock, and the shipping service directly to trigger delivery. If the shipping service fails, checkout can fail too, and customers cannot buy products.
In an event streaming architecture, the checkout service publishes an Order_Placed event to Kafka and moves on. The inventory service and the shipping service read that stream at their own pace. If the shipping service fails, checkout stays up. When the shipping service restarts, it reads the backlog of events from where it left off and processes them.
Real-Time Analytics
Event streaming is a primary way organizations move from reporting on the past to reacting to the present. Using stream processing engines like Apache Flink or Spark Structured Streaming, data teams consume Kafka events continuously. They compute aggregations on the fly (e.g., detecting if a credit card is used in two different countries within ten minutes), raise real-time alerts, or write refined data into an Apache Iceberg table for analytical querying shortly after the events occur.
Summary of Technical Value
Event streaming is core infrastructure for building a real-time, responsive organization. By capturing operational changes as continuous events and distributing them through a durable, replicated log, organizations decouple their microservices and lay the foundation for low-latency analytical applications.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.