Production debugger for Kafka and Kinesis pipelines

The moment an on-call engineer gets paged at 2am because consumer lag is spiking or messages are disappearing, they open their stream processor's dashboard and find almost nothing actionable. They can see that lag exists; they cannot see which messages are stuck, whether duplicates are being produced, or whether the issue is upstream (producer) or downstream (consumer). They end up grepping logs across multiple services with no unified view of what actually moved through the pipeline and when.

This gap persists because the stream processors themselves — Kafka, Kinesis, Flink — are infrastructure vendors. Their incentive is to make the pipe reliable, not to make your application-level bugs visible. Debugging at the message level is messy and involves the user's data schema, which the infrastructure vendor has no interest in touching. So they ship basic metrics (throughput, lag) and leave the rest to you.

What's actually missing is a layer that captures a time-windowed replay of message headers and routing decisions without storing the full payload (privacy-safe), correlates that against consumer acknowledgment records, and surfaces exactly where in a partition sequence a gap or duplicate occurred. The complaints are specific: 'data loss, consumer lag, duplicate messages, lost messages' and 'data will vanish if not consumed for a long time.' None of the existing infrastructure tools give engineers a way to reconstruct the sequence of events leading to those states.

This is a business because production incidents recur weekly in any company running real-time pipelines. Each incident without a clear root cause costs engineering hours and erodes trust in the pipeline. The need doesn't go away after the first fix — pipelines change, traffic patterns change, and new failure modes appear. An engineering team that adopts this stops flying blind every time something breaks.

What to build

Build a sidecar agent that attaches to Kafka and Kinesis consumers, records message offset sequences and acknowledgment timing in a lightweight local store, then surfaces a web UI showing exactly where in the partition timeline data loss, duplication, or lag acceleration occurred — correlated across producers and consumers in the same pipeline.

Where to start

Start with Kafka-on-Kubernetes shops where the agent can be deployed as a sidecar container in the same pod — this removes the 'we can't touch production infrastructure' objection because it's the same pattern they already use for logging agents.

The hard part

Getting the first team to install a sidecar agent in a production environment requires a level of trust that takes weeks to earn — you need a compelling demo environment that replicates real failure scenarios convincingly enough that engineers will push for internal approval.

How it makes money

Monthly subscription per cluster monitored, with a free tier for a single cluster with 7-day retention to drive adoption bottom-up through individual engineers.