Media event pipeline
WaveStream streams video to millions of viewers. Every play, pause, and quality switch generates an event, and the team is building a platform that turns those events into real-time analytics, billing, and personalization. They already run RabbitMQ for a handful of internal queues, but the new pipeline needs to absorb far more volume with replay and strict ordering.
Step 1 · Learn the domain
In Domain Context Learner, WaveStream captures:
| Prompt | Answer |
|---|---|
| Domain | Media & Streaming |
| Qualities that matter | Scalability, Resilience & uptime, Cost control, Time-to-market |
| Data volume | High — every play, pause, and quality switch |
| Real-time needs | High — analytics and personalization must react live |
| Integration complexity | Complex — analytics, billing, personalization, support |
| Sensitive data | Not sensitive — viewing telemetry, no PII required |
The profile pushes Architect toward scalability and resilience first, with cost efficiency ranked above compliance — telemetry, not regulated data.
Step 2 · Evaluate the architecture
In Architect, WaveStream opens the Event Streaming Platform scenario. With scalability and reliability weighted highest, the race is between the log-first and pub/sub-first patterns:
| Architecture | Why it ranks well here | Watch out |
|---|---|---|
| Apache Kafka | Log-based replay, per-partition ordering, massive throughput | Operational complexity to host or manage well |
| Amazon Kinesis Data Streams | Managed log semantics with ordering and replay | Cost at sustained very-high throughput |
| Google Cloud Pub/Sub | Fully managed pub/sub, at-least-once delivery | Weaker ordering guarantees than a partitioned log |
| Amazon EventBridge | Simple routing and filtering between consumers | Not built for replay-heavy, event-log workloads |
Because replay and ordering carry real weight, the log-first patterns lead the ranking. The interesting decision is managed vs self-hosted: if WaveStream's team can operate Kafka, its cost profile wins at this scale; if not, Kinesis trades cost for operational simplicity. That is exactly the tradeoff the What-If sliders make visible in one move.
The Q&A panel spells out the log-vs-queue differences in plain language — ordering and replay semantics are the two dimensions that separate these architectures.
Step 3 · Review the design you have
WaveStream's current pipeline is built on RabbitMQ fan-out. Running it through Reviewer against the same scenario exposes the gaps:
| Finding | Why |
|---|---|
| Low fit on replay | Messages are consumed and gone — no durable re-read |
| Low fit on ordering at scale | Broker-wide ordering collapses once consumers fan out |
| Low fit on throughput | Single-broker design needs heavy tuning at event-scale |
| Strength: operational simplicity | Well-understood broker, easy to run today |
The verdict is a clean justification for the migration: keep the RabbitMQ usage where queues genuinely fit, and stand up a log-first event backbone for the analytics, billing, and personalization consumers that need replay and ordering.
Want the full workflow before the examples? Read the Wahab Waypoint overview →