Modern applications have an unceasing buzz of user activity and data flows. Users send a flurry of one-click reactions to social media posts. Wearable tech and other IoT sensors work nonstop to transmit event data from their environments. Meanwhile, customers on e-commerce sites perform shopping cart actions or product searches which can bring immediate impact to operations. Today’s software organizations need the ability to process and respond to this rich stream of real-time data.
That’s why they adopt an event-driven architecture (EDA) for their applications.
Long gone are the days of monolithic applications with components tightly coupled into a single, bloated piece of software. That approach leads to scalability issues, slower development cycles, and complex maintenance. Instead, today’s applications are built on decoupled microservices and components — individual parts of an application that communicate and operate independently, without direct knowledge of each other’s definitions or internal representations. The resulting system is resilient and easier to scale and manage.
This is where EDA comes in. EDA enables efficient communication between these independent services, ensuring real-time data processing and seamless integration. With EDA, organizations leverage this decoupling to achieve the scalability and flexibility they need for their dynamic environments. And central to the tech stack for realizing EDA is Apache Kafka.
In this post, we’ll explore the advantages of using Kafka for EDA applications. Then, we’ll look at how Apache Kafka on Heroku simplifies your task of getting up and running with the reliability and scalability to support global-scale EDA applications. Finally, we’ll offer a few tips to help pave the road as you move forward with implementation.
Kafka’s Advantages for Event-Driven Systems
An EDA is designed to handle real-time data so that applications can respond instantly to changes and events. Boiled down to the basics, we can break down an EDA application to just a few key concepts:
- An event is data — often in the form of a simple message or a structured object — that represents something that has happened in the system. For example: a customer has placed an order, or a warehouse has confirmed inventory numbers for a product, or a medical device has raised a critical alert.
- A topic is a channel where an event is published. For example: orders, or confirmations, or vital signs.
- A producer is a component that publishes an event to a topic. For example: a web server, or a POS system, or a wearable fitness monitor.
- A consumer is a component that subscribes to a topic. It listens for a notification of an event, and then it kicks off some other process in response. For example: an email notification system, or a metrics dashboard, or a fulfillment warehouse.
Decoupling components
An EDA-based application primarily revolves around the main actors in the system: producers and consumers. With decoupling, these components simply focus on their own jobs, knowing nothing about the jobs of others.
For example, the order processing API of an e-commerce site receives a new order from a customer. As a producer in an EDA application, the API simply needs to publish an event with the order data. It has no idea about how the order will be fulfilled or how the customer will be notified. On the other side of things, the fulfillment warehouse is a consumer listening for events related to new orders. It doesn’t know or care about who publishes those events. When a new order event arrives, the warehouse fulfills the order.
By enabling this loose coupling between components, Kafka makes EDA applications incredibly modular. Kafka acts as a central data store for events, allowing producers to publish events and consumers to read them independently. This reduces the complexity of updates and maintenance. It also allows components to be scaled — vertically or horizontally — without impacting the entire system. New components can be tested with ease. With Kafka at the center, producers and consumers operate outside of it but within the EDA, facilitating efficient, real-time data processing.
Real-time data processing
Kafka allows you to process and distribute large streams of data in real time. For applications that depend on up-to-the-second information, this ability is vital. Armed with the most current data, companies can make better decisions faster, improving both their operational efficiency and their customer experiences.
Fault tolerance
For an EDA application to operate properly, the central broker — which handles the receipt of published events by notifying subscribed consumers — must be available and reliable. Kafka is designed for fault tolerance. It replicates data across multiple nodes, running as a cluster of synchronized and coordinated brokers. If one node fails, no data is lost. The system will continue to operate uninterrupted.
Kafka’s built-in redundancy is part of what makes it so widely adopted by enterprises that have embraced the event-driven approach.
Introduction to Apache Kafka on Heroku
Apache Kafka on Heroku is a fully managed Kafka service that developers — both in startups and established global enterprises — look to for ease of management and maintenance. With a fully managed service, developers can focus their time and efforts on application functionality rather than wrangling infrastructure.
Plans and configurations for Apache Kafka on Heroku include multi-tenant basic plans as well as single-tenant private plans with higher capacity and network isolation or integration with Heroku Shield to meet compliance needs.
With Apache Kafka on Heroku, your EDA application will scale as demand fluctuates. Heroku manages Kafka's scalability by automatically adjusting the number of brokers in the cluster, making certain that sufficient capacity is available as data volume increases. This ensures that your applications can handle both seasonal spikes and sustained growth — without any disruption or need for configuration changes.
Then, of course, we have reliability. Plans from the Standard-tier and above start with 3 Kafka brokers for redundancy, extending to as many 8 brokers for applications with more intensive fault tolerance needs. With data replicated across nodes, the impact of any node failure will be mitigated, ensuring your data remains intact and your application continues to run.
Integration Best Practices
When you design your EDA application to be powered by Kafka, a successful integration will ensure its smooth and efficient operation. When setting up Kafka for your event-driven system, keep in mind the following key practices:
- Define your data flow. As you begin your designs, map out clearly how data ought to move between producers and consumers. Remember that a consumer of one event can also act as a producer of another event. Producers can publish to multiple topics, and consumers can subscribe to multiple topics. When you’ve designed your data flows clearly, integrating Kafka will be seamless and bottleneck-free.
- Ensure data consistency and integrity. Take advantage of Kafka’s built-in features, such as transactions, topic and data schema management, and message delivery guarantees. Using all that Kafka has to offer will help you reduce the risk of errors, ensuring that messages remain consistent and reliably delivered across your system.
- Monitor performance and log activity: Use monitoring tools to track key performance metrics, and leverage logging for Kafka’s operations. Robust logging practices and continuous monitoring of your application will provide crucial performance insights and alert you of any system health issues.
Conclusion: Bringing It All Together with Heroku
In this post, we've explored how pivotal Apache Kafka is as a foundation for event-driven architectures. By decoupling components and ensuring fault tolerance, Kafka ensures EDA-based applications are reliable and easily scalable. By looking to Heroku for its managed Apache Kafka service, enterprises can offload the infrastructure concerns to a trusted provider, freeing their developers up to focus on innovation and implementation.
For more information about Apache Kafka on Heroku, view the demo or contact our team of implementation experts today. When you’re ready to get started, sign up for a new account.