What is event streaming?

A key function in event stream processing (ESP), event streaming enables IT infrastructures to handle large, continuous streams of events by processing data when the event or change happens. Event streaming is distinct from stream processing. Event streaming provides the infrastructure that moves and stores events. Stream processing might apply filtering, aggregation or enrichment to those events.

Event streaming often complements batch processing, which acts on large, static datasets (or “data at rest”). However, instead of processing bounded data in batches, event streaming can handle unbounded data as it arrives. This approach allows software within the architecture to interpret and respond to streams of data (“data in motion”) in real time.

High-performance event streaming services can power a range of tasks, from simple functions such as sending notifications when stock or product prices change to more complex workloads. They can also support artificial intelligence (AI) and real-time machine learning models that detect suspicious user activity. 

Even in the case of batch processing, event streaming can add depth to data analytics by connecting events with their respective timestamps. It also helps identify historical trends, providing valuable insights over time.

Event streaming revolves around the continuous and potentially unbounded flow of data records, called “events,” foundational data structures that record any occurrence in the system or environment. An event is an immutable record that something happened (such as an order being placed or a payment being authorized.) A “stream” (also called a data stream or streaming data) is the continuous delivery of those events.

Each event typically comprises a key that identifies the event or the entity it pertains to, along with a value that holds the actual data of the event. Also, it includes a timestamp indicating when the event occurred or was recorded and sometimes metadata about the data source, schema version or other attributes.

With the help of specialized stream processing engines, events can undergo a few different processes within a stream. “Aggregations” perform data calculations, such as means, sums and standard deviation. “Ingestion” adds streaming data to databases. Analytics processing uses patterns in streaming data to predict future events and enrichment processing combines data points with other data sources to provide context and create meaning.

As organizations generate large volumes of event data, they might use real-time analytics to identify patterns, anomalies and trends as they emerge. AI builds on these real-time insights by detecting complex relationships in event streams, generating predictions and recommending actions.

This capability is becoming a prerequisite for AI systems: According to IDC, 80% of agentic AI use cases require real time, contextual and widely accessible data. Whether supporting analytics, AI or automated workflows, these systems rely on a continuous flow of events that represent activity across an organization.

Events are tied to business operations or user navigation processes and typically trigger another action, process or series of events. Take online banking, for example. When a user clicks “transfer” to send money from one bank account to another, the funds are withdrawn from the sender’s account and added to the recipient’s bank account. Email or SMS notifications are sent to either (or both) parties and if necessary, security and fraud prevention protocols are deployed.

Events are the central component of event streaming. However, a set of other components enables streaming services to process events as quickly and effectively as they do. Other vital components include:

Brokers receive events from producers, organize and store them and make them available to consumers. By serving as intermediaries, brokers allow producers and consumers to communicate without direct connections, enabling each to operate and scale independently. 

In distributed event streaming systems, brokers ensure low latency and high availability by replicating events across multiple nodes. Brokers can also form clusters—sets of brokers working together for easier load balancing and scalability.

Topics are categorizations or feed names to which events are published, providing a way to organize and filter events within the platform. They act as the "subject" for events, allowing consumers to subscribe to topics and receive only relevant events.

Topics can be further divided into partitions, allowing multiple consumers to read from a topic simultaneously without disrupting the order of each partition.

An offset is a unique identifier for each event within a partition, marking the position of an event within the sequence. Consumers use offsets to organize the events they’ve processed. If, for instance, a consumer disconnects from an event and later reconnects, it can resume processing from the last known offset.

Given the proliferation of data—and the resulting surge of data traffic—event streaming is an essential component of modern data architectures. It is valuable in environments that require lightning-fast decision-making capabilities or in organizations aiming to automate decision-making responsibilities.

Here’s how event streaming services manage event data:

1. Event generation. Event streaming starts when producers (microservices backend systems, IoT ecosystems or event-driven APIs, for instance) send events to the event streaming platform.
   
   
2. Event publishing. Using client libraries, producers publish the event to a specific topic within the platform, at which point they become available for consumers (the apps and services that subscribe to the topic).
   
   
3. Event storage. The platform stores the events for a predetermined period, with brokers managing the event storage and retrieval processes.
   
   
4. Event consumption. Consumers process event data to initiate other events. Depending on their purpose or configuration, they can act upon events immediately as they arrive (real-time processing), store them for later processing or compile them for batch processing. Consumers also track their position in the data pipeline by using offsets, so they can resume processing from where they left off, when there is a failure or restart.
   
   
5. Event delivery. The event broker makes events available to consumers. It might guarantee event delivery under the “at least once” semantic, which would ensure that every event reaches its destination, even if duplicates occur. The process can also include “exactly once” delivery, ensuring each event is delivered once and only once or another type of semantics.
   
   
6. Event processing. Once the initial event is consumed and delivered, event data is processed for several downstream actions, including transforming the data, aggregating it and triggering complex event processing (CEP) workflows. Organizations commonly use stream processing technologies such as Apache Flink, Apache Spark Structured Streaming and Kafka Streams to perform these operations in real time across distributed event-driven systems. The resulting real-time data streams can also provide the continuous flow of data that AI systems use to support real-time decision-making.

Event streaming is typically implemented as a distributed architecture designed to capture, store, process and distribute continuous streams of event data across multiple systems. Rather than relying on direct point-to-point connections, event streaming architectures use shared infrastructure that decouples data producers from consumers, allowing applications to scale independently and exchange information in real time. Several core architectural components make this framework possible:

A key component of most event streaming architectures is an event broker, which acts as a durable, scalable backbone for event data. Events are written to topics within a distributed log, where they are stored in the order they are received and remain available for later consumption.

This architecture allows multiple applications to independently access the same event stream without requiring direct integration with the systems that produced the data. Technologies such as Apache Kafka, Amazon Kinesis and Apache Pulsar commonly provide this capability.

To support large-scale workloads, event streams are typically organized into topics and divided into partitions that can be distributed across multiple servers. Partitioning enables parallel processing and higher throughput, while replication creates copies of data across different nodes to improve fault tolerance and availability when individual components fail.

Event streaming architectures separate the systems that generate data from the systems that use it. Producers publish events to topics, while consumers subscribe to those topics and process events independently. This decoupled design allows new applications and services to be added without modifying the systems that generate the data.

Many event streaming architectures include a stream processing layer that analyzes and transforms events as they move through the system. Common stream processing technologies include Apache Flink, Kafka Streams, ksqlDB and Apache Spark Structured Streaming. These technologies support operations such as filtering, enrichment, joins and aggregations on continuously arriving data.

Some streaming workloads require applications to maintain information about past events. For instance, an e-commerce platform might analyze all purchases made during the past hour to identify trending products and update recommendations in near-real-time. State management enables the system to retain and update this information over time, while windowing organizes continuous data streams into time-based or event-based intervals for analysis.

Event streaming architectures typically include mechanisms that help prevent data loss and support recovery from failures. Depending on application requirements, systems can provide at-most-once, at-least-once or exactly-once processing guarantees. Checkpointing, replication and consumer offset tracking help maintain data consistency during interruptions or system restarts.

Processed events are commonly delivered to downstream destinations such as databases, data warehouses, data lakes, search indexes, machine learning systems or other event streams. These integrations allow event data to support real-time analytics, operational applications, automation workflows and AI applications.

In addition to standard streaming and processing, event streaming platforms, such as Amazon Kinesis, Google Cloud Pub/Sub, Azure Event Hubs and IBM Event Automation, facilitate a range of streaming practices that enhance functionality. IBM Event Automation, in particular, uses the processing power of the open source Apache Kafka platform to optimize event-driven workflows. These capabilities also help organizations deliver the real-time data pipelines that analytics and AI systems depend on.

Exactly-once processing means that each event in a stream is processed exactly once, an essential feature for preventing duplicate and lost stream events. Most event streaming systems include mechanisms to provide exactly-once semantics, regardless of failures elsewhere in the system.

When downstream components can’t keep up with the incoming event rate, backpressure prevents streams from overwhelming the system. With backpressure, a data flow control mechanism, consumers can signal producers to throttle or stop data production when they’re overwhelmed with data processing or unable to keep up with incoming events.

This process allows systems to gracefully handle workloads by buffering or dropping incoming events—instead of disrupting the entire system—so that event processing remains stable as workloads fluctuate.

Event consumers often work as part of a consumer group to accelerate event consumption. Each consumer in a consumer group is assigned to a subset of partitions, enabling parallel consumption for greater efficiency. If one consumer within the group fails or needs to be added or removed, the platform can dynamically reassign partitions to maintain balance and fault tolerance.

Event streaming often means processing data in a time-sensitive manner. A watermark is the system’s estimate of how far event time has progressed. Watermarking enforces a completeness threshold that indicates when the system can consider event data fully processed. Watermarking can also come in handy for ensuring accuracy in time-based processing and for reconciling out-of-order events.

Most event streaming platforms offer customizable data retention policies that allow developers to control how long events are available for consumption. However, data compaction is a process that removes redundant or obsolete data from topics, keeping the storage footprint minimal while preserving essential data.

It’s worth noting, again, that standard streaming architectures typically decouple event producers, event brokers and consumers so that components can be scaled and maintained independently. This flexibility enables companies to support many downstream applications, including real-time analytics, AI-powered decision-making and automated workflows. This flexibility enables companies to support many downstream applications, including real-time analytics, AI-powered decision-making and automated workflows.

The difference between event streaming and batch processing is that event streaming handles data continuously as events occur, while batch processing processes accumulated data periodically or when triggered.

Event streaming and batch processing both move and process data, but they differ primarily in when and how data is handled. Event streaming continuously captures and transports events from sources such as applications, databases and IoT devices as those events occur. Instead of waiting for a complete dataset, systems handle individual events or small groups of events as they arrive, supporting low-latency analysis and responses.

This approach is well suited to use cases in which the value of data depends on its freshness. Examples include fraud detection, operational monitoring, real-time recommendations and AI systems that rely on current information.

Batch processing collects data over a defined period and processes it together as a discrete workload. Because results do not need to be produced immediately, batch processing is appropriate for tasks such as generating daily reports, reconciling accounts or updating analytics dashboards.

In addition, the two approaches are complementary rather than mutually exclusive: an organization might use event streaming to detect and respond to activity as it happens, then use batch processing to perform comprehensive historical analysis on the accumulated data.

Event streaming is a powerful concept that allows organizations to use data as it’s generated, creating more responsive and intelligent systems. With the rise of data-driven decision-making, event streaming is an increasingly important component in modern software architectures.

As such, event streaming technologies have a range of use cases across business sectors, including:

Beyond specific industries, event streaming can also be useful when deployed in concert with other technologies and architectures. For instance, event streaming is sometimes associated with patterns such as event sourcing and command query responsibility segregation (CQRS).

Event sourcing is an architectural pattern wherein changes to the app state are stored as a sequence of events. Used alongside event streams, event sourcing allows the streaming system to replay events. This capability can reconstruct the state of an entity at any point in time or drive other components of the system.

In a CQRS architectural pattern, the system is split into two different models: one that handles commands (writing) and one that handles queries (reading). Event streaming can be used in CQRS to propagate changes from the write model to the read model in real-time, enabling asynchronous integration between the two.

Event streaming is also a foundational technology for building event-driven architectures. An event-driven architecture enables loosely coupled components to communicate through events. Instead of publishing streams of events to a broker, it publishes a single-purpose event that another app or service can use to perform actions in turn.

The in-stream processing power provided by event-driven architectures used along with event-streaming capabilities can enable businesses to respond to data in motion and make quick decisions based on current and historical data. This real-time responsiveness also supports analytics and AI applications that depend on continuously updated data.

In recent years, cloud providers have started to offer the principles of event streaming as services. Event streaming-as-a-service makes it easier for businesses to adopt event streaming without managing the entire underlying infrastructure, further broadening event streaming’s use cases.