Applicability
When to Use
✓When data needs to be processed with low latency
✓When continuous data streams need transformation
✓When real-time analytics or dashboards are required
Overview
How It Works
The Streaming Pipeline pattern processes data continuously as it arrives, rather than in batches. Using Kafka MCP Server as the backbone, data flows through transformation stages where each stage reads from one topic and writes to the next.
Each stage is an independent agent that reads from a Kafka topic, processes the data using other MCP servers (e.g., enrichment from a database, validation against a schema), and writes the result to the next topic. This enables real-time processing with built-in backpressure handling.
Implementation
Code Example
typescript
async function streamingPipeline() {
// Stage 1: Enrich raw events
kafka.consume("raw-events", async (message) => {
const event = JSON.parse(message.value);
const enriched = await enrichEvent(event);
await kafka.produce({ topic: "enriched-events", messages: [{ value: JSON.stringify(enriched) }] });
});
// Stage 2: Aggregate metrics
kafka.consume("enriched-events", async (message) => {
const event = JSON.parse(message.value);
await redis.incr(`metric:${event.type}:${currentHour()}`);
await redis.expire(`metric:${event.type}:${currentHour()}`, 86400);
});
}
async function enrichEvent(event) {
const user = await postgres.query("SELECT * FROM users WHERE id=$1", [event.userId]);
return { ...event, user: user.rows[0] };
}Quick Info
Categorydata-pipeline
ComplexityHard