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Connect your streams
Plug in Kafka, Kinesis, or Flink. No code changes to producers or consumers. Streamforge connects at the broker layer.
Schema-level anomaly detection for event streams
Self-healing data pipelines for event-driven systems
Streamforge detects schema drift and anomalies across Kafka, Kinesis, and Flink, then repairs the pipeline automatically before data loss spreads downstream.
What teams tell us after the first month
~94%
of schema drift events caught before downstream impact
based on pilot deployments
<90s
average time from anomaly detection to self-healing reroute
3-5 AM
peak hour for silent data loss, when no one's watching
The Problem
Event pipelines fail in ways your dashboards don't see
Schema drift breaks consumers silently. A producer starts sending null event IDs, your downstream data warehouse sees the row, accepts it, and quietly stores garbage. By the time anyone notices, you're auditing 48 hours of corrupt orders.
Datadog shows you CPU. Confluent Schema Registry enforces registered schemas at produce time. Neither one catches optional-field drift, type coercions in non-Avro topics, or volume anomalies from a producer going dark. Streamforge instruments every stream at the schema level, not the infra level. That is the gap we were built to close.
orders-v2.checkout / schema diff
| Field | Expected | Actual | Status |
|---|---|---|---|
| order_id | string | string | OK |
| event_id | string | null | DRIFT |
| amount | float64 | float64 | OK |
| currency | string | int32 | TYPE |
| timestamp | int64 | int64 | OK |
Everything that keeps a pipeline honest
Schema Guard
Field-level schema validation against rolling 30-day baseline. Catches null field creep, type coercions, and new unexpected keys before they hit consumers.
Self-Heal Routing
Define reroute rules once. When a topic breaches thresholds, Streamforge redirects to a quarantine stream. No ops intervention required.
Volume Anomaly Detection
Detects sudden message-rate drops or spikes. Silent loss from a downed producer is the failure mode that kills data warehouse freshness.
DLQ Inspector
Dead-letter queue with schema diff view. Every quarantined event shows exactly what field violated expectations. Replay cleaned events when the producer fixes the issue.
Alert Routing
PagerDuty, Slack, OpsGenie, email. One alert per incident, not one per failed message. Configurable dedup window.
Zero-Code Connect
Broker-layer connection, no producer SDK, no consumer code changes. Works with your existing Kafka ACLs and IAM policies.
From engineers who've been paged at 3 a.m.
We had a producer shipping nulls into a topic for 6 hours before anyone noticed. With Streamforge we'd have caught it in the first minute and rerouted automatically. That's the deploy I keep replaying.
Schema drift was our biggest blind spot. We run 80+ topics and had no systematic way to know when a producer team changed a field type. Streamforge Schema Guard is the answer.
The DLQ inspector alone is worth it. I can see exactly which field failed and replay the events once we fix the producer. Used to take 2 to 3 hours of logs spelunking.
Your next 3 a.m. incident could be the last one.
Connect your first pipeline in under 15 minutes. No credit card required.