What Is Event Streaming? Why Is It Growing in Popularity?

Event streaming has become an integral part of app development, real-time data processing and analysis, and leveraging data insights to create more engaging customer experiences. Whether you are an app developer, a data scientist, or a machine learning engineer, you need to implement event streaming into your systems to capture, integrate, access, and analyze data in real-time.

Companies like Amazon, Netflix, and Uber have been using microservice architectures with RESTful APIs and loose coupling to enable fast and reliable service delivery. However, microservice architectures are more expensive, go through complex development and integration testing, lead to many remote calls, and pose security threats.

That’s where event-driven architecture (EDA) comes in. It’s a paradigm that solves these challenges and improves the overall app performance. It uses event streams and messages to enable seamless communication between services, and you can add it to any environment to create an asynchronous system.

But what is event streaming, really? What are events in a distributed messaging system, and how can you benefit from an event-driven architecture? Let’s find out.

Key Components of Event-Driven Architectures

An event-driven architecture is one of the most popular ways of communication between back-end systems. It includes the following components:

• Events – state changes of an object due to users’ actions
• Event handlers – codes that run when events trigger, allowing the system to respond to changes
• Event loops – design patterns that handle interactions between events and event handlers
• Event flow layers:
  • Publishers/producers – apps, services, processes, databases, sensors, or users that generate events and publish messages to topics or channels;
  • Subscribers/consumers – apps, services, processes, databases, sensors, or users that consume events and can subscribe to receive messages from a certain topic;
  • Channels/topics – subsets of events in a single category that transfer events from producers to consumers;

Many companies use message brokers as well, that is, middleware software that stores and transmits events between producers and consumers in a publish-subscribe (pub/sub) architectural design pattern. That’s the most common model for transferring events that allow consumers to receive messages in order.

Message brokers can receive events from back-end systems, IoT devices, and event-driven APIs when there’s two-way communication. They can then send alerts to subscribers.

What Are Events in Programming and Software Design?

Events are data points that represent state changes in a system. When a user makes an action, they trigger an event, which doesn’t specify what should happen and how the change should modify the system. It only notifies the system of a particular state change.

For instance, when you make a financial transaction, you trigger an event that changes your account’s state. When you buy a product, its state changes from “For sale” to “Sold.”

Now, events and messages are often used interchangeably because services in a system communicate with one another through events. However, some messages can be events, while others only describe events.

For example, “send_welcome_email” is a message type schema that specifies what should happen when a user takes a certain action. It’s not an event.

But if you write “user_signup” in the schema, it becomes an event that could trigger other events, such as sending Welcome emails or adding subscribers to a CRM system.

What Are Event Streams?

Event streams are sequences of events in a pub/sub architectural design pattern. They are durable, persistent, and consist of immutable data. Temporal durability ensures that the system doesn’t remove events immediately after processing them, as is the case with message queues.

Queues store messages only until events are processed. Microservices and serverless architectures typically use message queues to decouple processing and control batch data or heavy data loads. However, message queueing doesn’t ensure message delivery in order. 

That’s why event messages are better for decoupling services, ensuring temporal durability, and managing system interactions more effectively.

What Is Event Streaming?

When you implement the pub/sub pattern in an event-driven architecture, you have event streaming. It provides real-time access to live, moving data, that is, a continuous flow of events that contain information on their state changes.

That moving data can come from software apps, mobile devices, IoT devices, cloud services, sensors, and various databases.

Thanks to temporal durability, event streaming allows systems to store data for later retrieval. That way, they can react to events both retrospectively and in real-time.

Some of the numerous real-world applications of event streaming include:

• Processing financial transactions (e.g., bank payments, insurance, and stocks)
• Processing orders (e.g., hotel bookings and retail shopping)
• Capturing and analyzing IoT data (e.g., to improve AI and machine learning models)
• Logistics tracking and reporting (e.g., tracking shipments and fleets)
• Storing and analyzing internal company data (e.g., streamlining communication and collaboration between departments).

Why Has Event Streaming Become So Popular?

Event streaming has exploded due to a demand for real-time interactivity, that is, leveraging events as they happen. It opens the door to a reactive UI design that allows users to be aware of their data at any given time. As a result, the user sees its effect immediately, no matter the gesture (clicks/taps, menu selections, or keystrokes).

Some of the popular examples include social media apps, Uber, Amazon, streaming services like Netflix, live transit updates, live sports score updates, video games, and live collaboration apps like Google Docs.

Another reason why event streaming is growing in popularity has to do with the various challenges of microservice architectures. Event streaming includes lightweight protocols that enable decoupling and prevent systems from turning into distributed monoliths.

The more microservices there are within a system, the more complex the workflow becomes. If you want to add a new service, you need to add tons of changes to the original workflow. With event streaming, you have an asynchronous functionality and loosely coupled structure, which means there are minimal modifications and easier integration testing.

Communication Protocols in Event-Driven Architectures

You can establish communication between back-end systems and web clients via event-driven APIs, powered by asynchronous or reactive event-driven protocols, such as Webhooks, WebSockets, SSE (Server-Sent Events), MQTT, etc.

Event-driven APIs deliver events to consumers and allow them to subscribe to events in various channels. They can also connect to message brokers.

The set of rules they follow are messaging protocols, which also include handling errors.

These are some of the most notable protocols and delivery models in event-driven architectures.

Broker/subscriber connection models

To set the connection between a broker and a subscriber, you can use broker-initiated and subscriber-initiated models.

The first model requires a broker to have information on existing subscribers. The broker can use a service-discovery mechanism to enable service requests or Webhooks (automated messages when a user registers, for instance).

The second model requires subscribers to know a particular broker’s address. It works well in dynamic subscriber interfaces or when you can’t use a service discovery mechanism.

Push-based and pull-based message consumption models

The push-based model (used by Apache Pulsar) aggregates events and lets the broker determine when it will send event messages to consumers, who don’t have to maintain the state. It’s great for rich UI environments, and you can use it through Webhooks and SSE.

The pull-based model (used by Apache Kafka) focuses on waiting for subscribers’ demands to pull data from the broker, which sends the data only when subscribers send a request to receive events. They have to maintain the state to know what data is available within certain topics.

These models are ideal for synchronous interfaces, as they can make them asynchronous and prevent an app from blocking the main UI thread while pulling data. That’s all thanks to their IEnumerable/IObservable duality.

Backpressure

Backpressure in event streaming refers to overloading consumers with information that they can’t process quickly. When publishers send events faster than consumers can process them, they create the backpressure that can overwhelm the system and negatively affect its overall performance.

There are many backpressure mechanisms for controlling a data influx and preventing systemic failures.

With the pull-based model, subscribers can notify the publisher when they can’t process more events. They can use error messages, pause event streaming temporarily, or limit the number of events to receive at once.

When it comes to the pull-based model, the publisher can send new events to subscribers only when they’re not actively processing events.

Combining backpressure mechanisms in reactive streams with seamless scalability is key for preventing data overloads, systemic failures, and other issues.

Message Delivery

Publishers have three main options for delivering event messages to subscribers.

The first option is to send all events within a channel or topic to all subscribers. For instance, chat apps can use this type of message delivery, sending events whenever a user receives a new message. MQTT is one example of a lightweight protocol that uses this pub/sub pattern model to connect subscribers.

The second option is to send events within a topic to only one subscriber. For instance, when a new app user signs up, the publisher can send a Welcome email to only that user. The first model would send multiple emails, whereas this one focuses on subscribers who share a state.

The third option includes grouping subscribers with a shared “id” and sending events to only one subscriber within a group. It requires implementing additional protocols for group message processing. Apache Kafka users Consumer Groups, assigning partitions of a topic to only one consumer in a group.

Top Benefits of Event Streaming

We’ve already touched upon some benefits of event streaming, but let’s explore them in more detail.

Decoupling services

When you decouple services in event streaming, that is, decouple publishers from subscribers, you can scale apps easily. You can add new services with minimal system modifications.

Publishers can produce as many events as they need, while subscribers can control what events they want to consume, how often to receive them, and how much data they want to process. All that data stays in a durable stream for a configured time.

Apache Pulsar and Apache Kafka are excellent examples of fully-decoupled services that are autonomous and unaware of one another, thus performing tasks independently and efficiently.

Better system and data reliability

With event streaming, systems can perform much better and provide more reliable data. That’s mainly thanks to decoupling and implementing exactly-once processing semantics to prevent data duplicates.

For instance, both Apache Pulsar and Apache Kafka use exactly-once semantics, thus guaranteeing that every message is delivered only once.

Since event streams are durable, there’s no risk of data loss. Events stay in the stream even after being processed.

Event streaming also solves issues regarding uptime and response time, robustness, and potential outages, which is particularly important when scaling apps.

Higher team efficiency

This is another benefit of decoupling. When you decouple services, multiple developers and other teams can work independently on various apps, even when they’re working on the same events. That way, they can be more efficient and cut down costs.

When they work with a message broker like Apache Pulsar or Apache Kafka, they can streamline the communication between publishers and subscribers, without having to program various services to communicate with each other.

Real-time feedback and data processing

No matter how high data volumes are, event streaming will provide real-time feedback. It will enable users to see their actions’ effects immediately, such as when sending a payment. They will typically receive an update on their transaction and balance within a minute.

Event streaming enables publishers to send events to consumers in real-time, so that they can send instant messages to users and respond to their actions. So whether it’s an email, an error message, or anything else relevant, every party can send and receive all event status and response information in real-time.

What Is Event Stream Processing?

Quite self-explanatory, event stream processing involves processing continuous data streams in asynchronous systems with an event-driven architecture. Events are processed as they occur from various event sources, such as IoT devices and apps.

Event stream processing is useful for tracking website activity, such as visits, clicks, and page views, to gather customer data and drive valuable insights for better user experiences. Businesses can use that information to uncover trends and find out what their most popular products are, for instance.

This type of processing can help detect patterns in event streams, aggregate log files, publish IoT messages, send messages to social media news feeds, and much more.

Stream processing vs. batch processing

Batch processing was a common practice until stream processing came into the spotlight. Many technologies still use it, but it’s more convenient for static data processing.

With batch processing, you need to store data and stop collecting it temporarily while you

process a batch, and then repeat everything for all the next batches. That’s not very time-efficient, especially when there’s a never-ending event stream.

If a company handles substantial data volumes, it can be impossible to store everything until it’s time to process it. Such buildups may require a lot of hardware as well.

Stream processing solves all these problems. It enables processing data as it comes in, thus saving time, preventing system overloads, and providing instant, accurate results.

It’s much better for processing time series data, such as IoT data (e.g., traffic sensors, temperature sensors, and transaction logs).

The most common example of stream event processing includes financial transactions. Banks use it to notify customers of state changes in their accounts in real-time. When a customer makes a payment, they can see a balance update almost instantly. If banks were to process transactions in batches (e.g., every 24 hours), customers wouldn’t be able to get real-time feedback.

Popular Event Streaming Technologies

Some of the most popular event streaming platforms are Apache Pulsar and Apache Kafka.

Apache Pulsar is a cloud-native, distributed messaging and streaming platform, while Apache Kafka is an open-source distributed event streaming platform. Both provide a broker architecture but use different message consumption models, as previously discussed.

Both platforms have SQL engines, options for sourcing and persisting data, and the same open-source server for cluster coordination (Apache Zookeeper). They also allow replicating clusters across multiple data centers.

When it comes to scaling clusters, Kafka uses brokers, while Pulsar uses Apache BookKeeper, which is a low-latency storage service optimized for real-time workloads.

Free and out-of-the-box tiered storage, lower latency, better throughput, better resource utilization, higher durability, and a proxy layer for service discovery is where Pulsar shines and outperforms Kafka.

Apart from Kafka, other popular message brokers include Apache ActiveMQ, RabbitMQ, Amazon MQ, Amazon Kinesis, Microsoft Azure Service Bus, and Google Cloud Pub/Sub. These last three include event streaming technologies, as well. You can also integrate them with various third-party cloud services for event stream processing.

Conclusion

Event streaming and processing have become crucial for building reliable systems and solving common design challenges. Therefore, they are bound to keep growing in popularity and bring more changes to event-driven architectures.

If you’re interested in a distributed messaging service built on the leading Apache Pulsar technology, check out Pandio, an AI-ready hosted solution that combines event streams, queues, and the pub/sub design pattern. Sign up today for a free Pandio trial.