ChatGPT, help me build a webhook pipeline that filters, transforms, and routes events to different services.

Absolutely — a webhook pipeline usually has 4 parts:

1. Receive webhook events
2. Validate + filter them
3. Transform/enrich the payload
4. Route to one or more downstream services

Here’s a practical blueprint you can use.

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1) High-level architecture

Webhook Source → Ingress API → Filter/Transform Layer → Router → Destinations

Example flow:

• A provider sends POST /webhooks/events
• Your service:
  • verifies signature
  • checks event type
  • drops unwanted events
  • normalizes the payload
  • routes it to:
    • Slack for alerts
    • CRM for customer events
    • Queue for async processing
    • Analytics sink for tracking

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2) Core design principles

A. Fast acknowledgement

Webhook senders often retry if you respond slowly.
Return 2xx quickly after:

• validation
• basic filtering
• persisting the event or queueing it

Then process downstream asynchronously.

B. Idempotency

Webhooks are frequently duplicated.
Store an event_id and ignore repeats.

C. Separation of concerns

Keep these separate:

• ingestion
• filtering rules
• transformation
• routing
• delivery retries

D. Observability

Log:

• event id
• source
• destination
• result
• latency
• retry count

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3) Suggested pipeline stages

Stage 1: Ingress

• Accept JSON webhook
• Validate authentication/signature
• Normalize headers and metadata

Example checks:

• HMAC signature
• timestamp freshness
• source IP allowlist
• content type

---

Stage 2: Filter

Filter based on rules like:

• event type
• source
• tenant/customer
• severity
• payload fields

Examples:

• only process invoice.paid
• ignore test mode events
• drop events missing customer_id

---

Stage 3: Transform

Map source-specific payloads into a standard internal event format.

Example transformation:

• user.email → customer.email
• created_at → ISO timestamp
• enrich with internal tenant config or lookup data

---

Stage 4: Route

Send the transformed event to one or many targets.

Routing patterns:

• fan-out: send to all matching destinations
• conditional routing: send based on rules
• priority routing: critical events first
• dead-letter routing: failed events to a DLQ

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4) Example internal event format

{
  "event_id": "evt_123",
  "source": "stripe",
  "type": "invoice.paid",
  "tenant_id": "t_001",
  "timestamp": "2026-10-05T12:00:00Z",
  "data": {
    "customer_id": "cus_456",
    "amount": 4999,
    "currency": "usd"
  },
  "metadata": {
    "environment": "prod"
  }
}

---

5) Routing rules example

You can define rules in config or a database:

[
  {
    "match": { "type": "invoice.paid" },
    "destinations": ["billing-service", "slack-alerts"]
  },
  {
    "match": { "type": "user.created", "tenant_id": "t_001" },
    "destinations": ["crm-service"]
  },
  {
    "match": { "severity": "high" },
    "destinations": ["pagerduty"]
  }
]

---

6) Simple implementation pattern

Option A: Single service + queue

Good for most cases.

Flow:

• Webhook API writes event to DB/queue
• Worker processes filter/transform/route
• Retry failures automatically

Suggested tools:

• API: FastAPI / Express / Go / Flask
• Queue: SQS / RabbitMQ / Kafka / Redis Streams
• Storage: Postgres
• Observability: OpenTelemetry + logs

---

7) Example pseudo-code

Ingress handler

def webhook_handler(request):
    verify_signature(request)
    event = request.json()

    if is_duplicate(event["event_id"]):
        return {"status": "ok", "duplicate": True}

    save_raw_event(event)
    enqueue(event["event_id"])

    return {"status": "accepted"}

Worker

def process_event(event_id):
    event = load_raw_event(event_id)

    if not should_process(event):
        mark_skipped(event_id)
        return

    transformed = transform_event(event)
    destinations = route_event(transformed)

    for destination in destinations:
        send_to_destination(destination, transformed)

    mark_done(event_id)

---

8) Example filter/transform/router logic

def should_process(event):
    return (
        event.get("type") in {"invoice.paid", "user.created"} and
        event.get("metadata", {}).get("environment") == "prod"
    )

def transform_event(event):
    return {
        "event_id": event["event_id"],
        "type": event["type"],
        "customer_id": event["data"].get("customer_id"),
        "amount_usd": event["data"].get("amount", 0) / 100,
        "timestamp": event["timestamp"]
    }

def route_event(event):
    destinations = []

    if event["type"] == "invoice.paid":
        destinations.append("billing-service")
        destinations.append("slack-alerts")

    if event["amount_usd"] > 1000:
        destinations.append("finance-review")

    return destinations

---

9) Reliability features you’ll want

• Retry with exponential backoff
• Dead-letter queue
• Timeouts per destination
• Circuit breaker for flaky services
• Rate limiting
• Schema validation
• Versioned payloads
• Replay capability for failed events

---

10) Security checklist

• Verify webhook signatures
• Rotate secrets
• Reject old timestamps
• Validate JSON schema
• Sanitize outbound payloads
• Limit payload size
• Store raw payloads securely
• Separate tenants logically

---

11) If you want rule-based routing

A good pattern is a small routing DSL or config like:

rules:
  - name: invoice-paid
    when:
      type: invoice.paid
    send_to:
      - billing-service
      - slack-alerts

  - name: vip-customer
    when:
      type: customer.updated
      customer_tier: vip
    send_to:
      - account-manager

Then your router evaluates rules in order.

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12) Recommended minimal stack

If you want to build this quickly:

• API: FastAPI
• Queue: Redis Streams or SQS
• Worker: Celery / Dramatiq / custom consumer
• DB: Postgres
• Validation: Pydantic / JSON Schema
• Delivery: HTTP client with retries

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13) A good MVP plan

MVP v1

• Receive webhook
• Validate signature
• Store raw event
• Queue processing job
• Filter by event type
• Transform into internal schema
• Route to 2 destinations
• Retry failures

MVP v2

• UI/config for routing rules
• Per-destination auth
• Replay failed events
• Metrics dashboard

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If you want, I can next give you one of these:

1. a concrete implementation in Python/FastAPI,
2. a Node.js/Express version,
3. a system design diagram, or
4. a database schema + routing rules model.