Case Study: Media Streaming
Global Media Ingestion Pipeline
A webhook-triggered event pipeline processing 300+ events per minute, distributing data in a fan-out pattern to the services that create customer support tickets.
The client is a media streaming company with a support team that monitors social mentions on X. When the company's account is tagged, that mention often requires action from customer service. The problem was that the process for routing those mentions to the right internal systems was manual and could not keep up with volume as the company grew.
They needed a system that could receive social events automatically, distribute the data to every internal service that required it, and do so reliably without manual steps between the trigger and the outcome.
300+
events per minute
Sustained throughput under normal operating load
Fan-out
distribution architecture
Each event reaches every downstream consumer independently
Webhook-triggered
end-to-end pipeline
From a social tag on X to a customer support ticket
The problem
The client's support team was receiving social mentions on X that required routing to multiple internal systems simultaneously. The volume of those mentions was not predictable, and the existing process was manual and slow. As the company's social presence grew, the gap between a mention arriving and the support team acting on it widened.
The client also needed the data from each mention to reach several different downstream services at the same time, since each service played a different role in the customer support workflow. A simple point-to-point integration would not scale as new consumer services were added.
What we built
We designed a webhook-triggered event pipeline that receives a signal each time the client's account is tagged on X. Each inbound event is validated and placed into a processing queue, which acts as a buffer that absorbs spikes in volume and ensures that no event is dropped if a downstream service is temporarily slow or unavailable.
From the queue, each event is distributed in a fan-out pattern to the downstream consumer services. Each consumer receives its own independent copy of the event and processes it at its own pace. The consumer services use the event data to create customer support tickets in the client's support system.
The pipeline processes more than 300 events per minute under normal operating conditions. Because the fan-out architecture decouples the pipeline from the consumers, adding a new consumer service downstream does not require changes to the core pipeline.
How the pipeline is structured
The diagram below shows the full event path from trigger to consumer.
TRIGGER
X Tag
INGEST
Webhook API
BUFFER
Event Queue
CONSUMER
Service A
CONSUMER
Service B
CONSUMER
Service C
TRIGGER
X Tag
INGEST
Webhook API
BUFFER
Event Queue
CONSUMER
Service A
CONSUMER
Service B
CONSUMER
Service C
Each inbound X event is buffered in the queue and delivered independently to all downstream consumer services.
The result
The pipeline handles high event volumes reliably without requiring manual intervention. The support team now receives structured ticket data automatically, and the time between a social mention arriving and a ticket being created is consistent regardless of volume. The infrastructure has processed more than 300 events per minute and continues to operate within those parameters.
The fan-out architecture also means that the client can add new downstream consumer services as their support workflow evolves, without revisiting the core pipeline design.
For additional context on scale: in a separate engagement for a nationwide education financing company, we have seen this type of architecture perform under loads of 4,000 requests per second. The 300-event-per-minute figure for this client reflects the load that engagement required; it is not the ceiling of what this type of architecture can handle.
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Building something similar?
Event pipelines, webhook integrations, and fan-out architectures each have specific design considerations that become more important as volume increases. If you are working through a similar problem and want to talk through the approach, then will you share the details through our contact page?