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Event Driven Triggers Basics

Learn Event Driven Triggers Basics for free with explanations, exercises, and a quick test (for MLOps Engineer).

Published: January 4, 2026 | Updated: January 4, 2026

Table of Contents

Why this matters

In MLOps, work should run when it needs to—not just on a schedule. Event-driven triggers start pipelines when something meaningful happens: a new dataset lands, a model registry changes, or a metric crosses a threshold. This reduces latency, saves compute, and keeps models fresh.

  • Automatically run a feature pipeline when a new partition appears in object storage.
  • Kick off model retraining when a performance metric drops.
  • Promote a model when a registry event signals a new approved version.
  • Fan-out batch scoring when a message arrives per customer segment.

Concept explained simply

An event-driven trigger is a rule that listens for an event (a fact that something happened) and starts the right task or workflow in response.

Mental model

Think of a smart doorbell. Someone presses it (event), your home hub recognizes it (router), and your camera and lights turn on (handlers). You control what counts as a press, who is allowed to ring, and what happens next.

Key terms
  • Event: A record of something that happened (file created, message published, webhook called).
  • Event source: Where events originate (object store, message broker, HTTP webhook, registry).
  • Router/broker: Moves events to subscribers; may filter, batch, or retry.
  • Trigger: Condition in your orchestrator that starts a task/flow when events match.
  • Handler: The function/task that runs because of the event.
  • Idempotency: Running the same event twice leads to the same result.
  • Dead-letter queue (DLQ): A holding area for events that fail repeatedly.
  • Delivery semantics: At-least-once (common), at-most-once, exactly-once (rare, simulated).

Core building blocks

  • Event sources: object storage notifications, message queues, pub/sub topics, webhooks (e.g., CI, registry), database change streams, metric alerts.
  • Trigger types: file/object created, message received, HTTP call, time-window + event (hybrid), threshold crossed.
  • Filters: path or prefix filters, event attribute filters (e.g., eventType=="created"), schema validation.
  • Fan-out/fan-in: One event can spawn many tasks; later you aggregate results.
  • Reliability: retries with backoff, DLQ, deduplication using event IDs, idempotent tasks.
  • Observability: structured event logs, correlation IDs, metrics for lag, age, throughput, failures.
  • Security: least-privilege policies for sources and handlers; verify webhooks (signature), validate payloads.

Worked examples

Example 1: New data arrives → feature pipeline
  1. Event source: Object storage emits "object-created" for path data/transactions/2026-01-04/*.parquet.
  2. Router: Filters for prefix data/transactions/.
  3. Trigger: Orchestrator starts the feature pipeline with event payload including object key and checksum.
  4. Handler: Pipeline computes features only for that partition.
  5. Reliability: Deduplicate by (bucket,key,etag). Retry 3 times, then send to DLQ.
Example 2: Model approved in registry → deploy
  1. Event source: Model registry emits "model.version.stage_changed" to "Production".
  2. Trigger: Orchestrator deploys the new version to staging, runs smoke tests, then promotes to production if checks pass.
  3. Idempotency: Promotion task uses modelVersionId as idempotency key.
Example 3: Metric alert → rollback
  1. Event source: Monitoring system emits "latency_p95_above_threshold" with model name and version.
  2. Trigger: Orchestrator runs rollback workflow: route traffic to previous version, notify owners, create incident ticket.
  3. Safety: Enforce a circuit breaker to avoid flapping (cooldown window).

Implementation steps (zero to one)

  1. Define the event: Name, when it fires, and payload schema. Include IDs to deduplicate: source, objectKey, etag/checksum, timestamp, version.
  2. Choose the delivery path: Direct webhook to orchestrator, or via a broker/topic for buffering and retries.
  3. Set filters: Prefix/path filters, event type filters, optional JSON schema validation.
  4. Configure the trigger: In your workflow engine, subscribe to the topic/event and map payload fields to task params.
  5. Make handlers idempotent: Use a persistent store to record processed event IDs. If seen, skip or update safely.
  6. Add retries and DLQ: Exponential backoff (e.g., 1m, 5m, 30m). Send permanently failing events to DLQ for inspection.
  7. Secure it: Limit source permissions; verify webhook signatures; avoid running on untrusted payloads without validation.
  8. Observe: Emit metrics (events processed, failures, age), logs with correlationId, and alerts for DLQ growth.
Event payload template (JSON)
{
  "eventType": "object.created",
  "source": "object-store",
  "bucket": "data",
  "key": "transactions/2026-01-04/part-000.parquet",
  "checksum": "e3b0c442...",
  "sizeBytes": 52428800,
  "contentType": "application/parquet",
  "timestamp": "2026-01-04T10:15:00Z",
  "correlationId": "tx-2026-01-04-000",
  "attributes": {"partition": "2026-01-04"}
}

Exercises

Complete the exercise below. You can check your progress in the Quick Test at the end. Test is available to everyone; only logged-in users get saved progress.

Exercise 1: Design an event-driven trigger for new data arrival (id: ex1)

Scenario: A new partition file arrives under data/sales/YYYY-MM-DD/. You must trigger a feature generation flow only for that partition with safe retries and idempotency.

  • Define the event schema with key fields.
  • Choose an idempotency strategy and deduplication key.
  • Specify retry/backoff policy and DLQ rule.
  • Describe how the orchestrator maps event fields to task parameters.
  • State the minimal security controls.
Expected output format
  • JSON payload schema.
  • Idempotency/dedup plan.
  • Retry + DLQ policy.
  • Parameter mapping.
  • Security notes.
Hints
  • Include a stable checksum or etag and correlationId.
  • Store processed event IDs in a durable store keyed by (bucket,key,checksum).

Common mistakes and self-check

  • Missing idempotency: Re-running the same event corrupts aggregates. Self-check: Can I safely process the same payload twice?
  • No DLQ: Poison events keep retrying forever. Self-check: Where do permanently failing events go?
  • Overbroad triggers: Filters too wide cause noisy runs. Self-check: Do filters match only needed prefixes and event types?
  • Hidden coupling: Handlers rely on implicit context. Self-check: Is everything needed in the payload or resolvable from it?
  • No backpressure: Spikes overwhelm workers. Self-check: Do I have concurrency limits and buffering?
  • Weak security: Unverified webhooks. Self-check: Are signatures validated and permissions least-privilege?

Self-check checklist

  • Event schema documented and versioned.
  • Filters configured and tested.
  • Idempotency key chosen and persisted.
  • Retries with exponential backoff and DLQ defined.
  • Metrics: eventsProcessed, failures, eventAge, dlqDepth.
  • Security: signature verification (for webhooks) and least privilege IAM.

Mini challenge

Design a fan-out pattern: One "batch-ready" event should trigger parallel scoring for 5 regions, then a final aggregation task only after all five complete. Describe:

  • Event schema additions needed to identify batch and regions.
  • How you correlate regional tasks to a final fan-in step.
  • Idempotency and retry behavior for both fan-out and fan-in.
Tip

Use a shared correlationId for the batch and regionId for each child task; aggregate waits on all regionIds.

Who this is for

  • MLOps Engineers implementing production pipelines.
  • Data Engineers moving from cron-based jobs to event-driven flows.
  • ML Engineers deploying models that react to data and metrics in real time.

Prerequisites

  • Basic understanding of workflow engines (e.g., concepts like tasks, DAGs/flows).
  • Comfort with JSON and environment variables/secrets.
  • Familiarity with object storage and message queues is helpful.

Learning path

  1. Start with event concepts and reliability (this lesson).
  2. Implement a storage-triggered feature pipeline.
  3. Add idempotency, retries, and DLQ to harden.
  4. Introduce metric-triggered rollbacks and CI/CD webhooks.
  5. Scale with fan-out/fan-in and backpressure controls.

Practical projects

  • Project 1: Storage-triggered partition processing with deduplication and DLQ.
  • Project 2: Registry-triggered blue/green model deployment with smoke tests.
  • Project 3: Metric-triggered rollback with cooldown and incident notification.

Next steps

  • Harden your trigger with schema validation and signature verification.
  • Add observability: dashboards for eventAge and dlqDepth.
  • Extend to fan-out/fan-in patterns and concurrency limits.

Save progress and Quick Test

Take the quick test below to check understanding. Available to everyone; only logged-in users get saved progress.

Practice Exercises

1 exercises to complete

Instructions

Scenario: A new partition file arrives under data/sales/YYYY-MM-DD/. You must trigger a feature generation flow only for that partition with safe retries and idempotency.
  • Define the event schema with key fields.
  • Choose an idempotency strategy and deduplication key.
  • Specify retry/backoff policy and DLQ rule.
  • Describe how the orchestrator maps event fields to task parameters.
  • State the minimal security controls.
Expected Output
1) JSON payload schema. 2) Idempotency/dedup plan. 3) Retry + DLQ policy. 4) Parameter mapping. 5) Security notes.

Event Driven Triggers Basics — Quick Test

Test your knowledge with 8 questions. Pass with 70% or higher.

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