Event-driven architectures can be challenging. However, when you overcome those challenges and embrace a self-serve approach to spinning up instantly discoverable, event-driven data feeds, you suddenly open up a whole heap of business benefits. By bringing a self-serve model to your event-driven architecture, you can accelerate innovation, improve visibility, enhance governance and reduce your operational costs. Let us show you how.
The challenges of event-driven architectures
First, let’s acknowledge some of the challenges of event-driven architectures.
Complexity overload is chief among them. It’s all too easy for event-driven architectures to become an intricate web of microservices, brokers (like Kafka), consumer applications and specialized tooling. This means your teams often waste time building (and maintaining) custom integrations just to mediate between protocols or parse payload formats.
Governance gaps are also a danger. After all, when multiple internal teams want access to real-time data, managing credentials and permissions natively in Kafka can become a headache. Add in each team’s application stack demanding unique language-specific libraries or network routing and you’re facing a significant time drain, as well as a governance challenge.
Then there’s the issue of slow onboarding. Providing new teams with access to event data usually requires coordination between platform engineering, DevOps, and security teams. This extended process creates bottlenecks, delaying time-to-market for features and new products.
The benefits of moving to a self-serve model
Keeping the above challenges in mind, imagine how much you could gain if your organization could quickly spin up event-driven data feeds—straight out of Kafka or any other broker—without writing custom code or wrestling with complicated ACLs. And what if they could make these feeds instantly discoverable for internal teams? Doing so solves the challenges of complexity overload, governance gaps and slow onboarding, delivering a range of benefits:
- Accelerated innovation: By removing friction in accessing event-driven data, you can empower teams to launch new features, run experiments, and iterate rapidly. No more waiting for microservices or Kafka ACL changes to be built and approved.
- Improved visibility and governance: By centralizing logs and metrics for both synchronous and asynchronous APIs (which you can do with Tyk Streams – more on that in a moment), you can track who’s consuming what data for easier compliance audits. You can also observe usage spikes or suspicious activity for deeper insights.
- Lower operational costs: When you implement a self-serve model that cuts through the complexity, you need fewer custom services, and maintenance becomes easier, resulting in tangible cost savings. These include a decreased DevOps overhead, as there’s no specialized bridging or transformations to maintain, and the ability to reallocate your engineering effort to focus on core product development instead of custom Kafka integrations.
If you adopt Tyk Streams to bring a self-serve model to your event-driven architecture, you can also consolidate reporting in Tyk’s analytics, or export to external observability platforms like Datadog, Elastic, or New Relic. Everything is visible in Tyk’s logs and dashboards, so you can streamline your incident response processes (and costs) as well.
Who stands to gain?
Plenty of people gain when event-driven meets self-serve. Your platform engineers and DevOps will appreciate the simplified deployment model, pre-built transformations and unified security controls.
Your internal developers and team leads will also be happy, as they can access event-driven data instantly without dealing with the nuances of Kafka. They can code in whatever language they prefer, using standard HTTP or SSE subscriptions.
Bringing a self-serve model to your event-driven architecture is also a win for solution architects and enterprise architects. It helps achieve consistent governance across all APIs (sync and async), reducing fragmentation and future-proofing the architecture.
Finally, your product managers will thank you for providing faster time-to-market for event-driven features and services, with an easy path to prototypes and experiments.
How Tyk Streams can help
We’ve mentioned Tyk Streams a couple of times now – and for good reason. It’s a powerful new feature in the Tyk API management platform that enables you to securely expose, manage and monetize real-time event streams and asynchronous APIs. It means you can help your engineers focus on what matters most – delivering value – rather than wrestling with infrastructure.
Tyk Streams flips the traditional approach on its head by letting you expose event-driven data feeds through a user-friendly portal, governed by the same policies that secure and manage your REST and/or GraphQL APIs.
Implementing a self-serve portal for event streams in this way provides you with a range of benefits:
- Single configuration, multiple protocols: With Tyk Streams, your platform engineers can configure a Kafka feed (or any other supported input) and expose it over standard HTTP, WebSocket (WS) or Server-Sent Events (SSE) – whichever suits your consumers. By abstracting away broker-specific details, Tyk Streams eliminates the need for specialized client libraries, enabling any internal team to subscribe using standard web protocols.
- Secure, consistent governance: Tyk’s familiar API policies – such as rate limiting, authentication, authorization and request validation – now apply seamlessly to your event-driven data. Teams can self-subscribe to streams, request keys or tokens, and see usage insights without direct Kafka ACL configuration.
- Discoverability via the Tyk Developer Portal: Internal teams no longer have to hunt for APIs or request side-channel documentation. Tyk’s developer portal lists available streams, displays documentation in a recognizable format (including AsyncAPI if desired) and makes it straightforward for teams to sign up and start receiving data.
How Tyk Streams simplifies internal collaboration
Ready for onboarding in minutes (rather than weeks), automatic policy enforcement and a reduced operational burden?
Using a traditional approach, onboarding would mean identifying the event topic in Kafka, building custom microservices to adapt the data stream to HTTP/WebSocket and then managing complex broker ACLs, setting up authentication and tracking usage manually. Plus providing each team with specialized documentation or client libraries. Instead, with Tyk Streams:
- Step 1: Open Tyk Dashboard
- Step 2: Select Kafka as the input and WebSocket as the output
- Step 3: Apply existing (or new) authentication and rate-limiting policies
- Step 4: Publish the stream to the developer portal
Now any internal team can log into the portal, browse available streams, request or generate a key to access the data and start consuming real-time events. It’s taken minutes to achieve something that would previously have taken weeks.
Additionally, Tyk Streams centralizes policy enforcement, so you don’t have to implement separate authentication and authorization logic in each microservice. This provides consistent security across synchronous and asynchronous APIs, no chance of mismatched security rules between teams and one central place for rotating keys, regenerating credentials and auditing usage.
Tyk Streams further simplifies internal collaboration by removing the need for custom microservices for bridging Kafka to internal teams, meaning you have less infrastructure to maintain. This frees up time and resources, with fewer deployment pipelines (as there are no more customer bridging services) and lighter maintenance, with Gateway rules and transformations configured in Tyk’s user interface. It means teams can direct efforts toward business logic, not infrastructure scaffolding.
A quick technical example
Imagine you have an orders topic in Kafka, producing events that reflect new purchases in real time. Multiple internal teams want to:
- Track incoming orders for inventory updates
- Analyze sales data in real-time dashboards
- Send notifications or follow-up emails to customers
Previously, using manual scripting and services, you would approach this by each team either building a microservice to consume orders from Kafka or relying on a “middleman” service. This often means unifying message formats, creating new ACL rules and duplicating a lot of code.
With Tyk Streams, you simply:
1. Configure a stream:
- Input: Kafka topic orders
- Output: SSE endpoint for real-time updates
- Apply transformations if needed (e.g. Avro-to-JSON)
2. Add governance:
- Use existing Tyk policies for rate limiting and access control
- Teams automatically receive usage analytics via the Tyk dashboard
3. Publish to portal:
- Teams discover an “Orders Real-Time Stream” in the developer portal
- They generate keys or tokens, subscribe to SSE, and build integrations in the language of their choice—no specialized Kafka client libraries needed.
Next steps: Bring self-serve to your organization
Event-driven architectures unleash real-time innovation. But as they scale, the complexity can hinder their full potential. Tyk Streams brings a self-serve, API-managed approach to your Kafka or other broker-driven data flows. It eliminates messy overhead, gives development teams easy on-ramps to real-time data, and centralizes governance in a unified portal.
If you’re ready to bring self-service to your event-driven architecture, you can check out the Tyk Streams documentation to see how quickly you can set up your first event stream.
You can use Tyk’s policy engine to unify security settings across all your APIs, event-driven or otherwise, then publish your internal event streams in Tyk’s Developer Portal. You can even expose your event streams externally for partners or customers, safe in the knowledge that Tyk will handle authentication, rate limits and analytics.
If you’re serious about accelerating your time to value with event-driven systems, and ensuring every team can tap into your streams without friction, you can read our quick start guide and get started here.
