Kafka: The Nervous System of Your Data Platform
High-throughput, fault-tolerant event streaming. Kafka connects everything—producers, consumers, real-time pipelines. Built for millions of events per second.
What we deliver with Kafka
Topic and schema governance
Naming standards, partition counts sized to real throughput, and Avro or Protobuf contracts with compatibility rules enforced by a registry.
Producer and consumer reliability
Idempotent producers, correct partitioning keys, consumer groups with dead-letter topics, and replay procedures that are tested rather than assumed.
Operations and observability
Consumer lag alerting, broker and disk health, rebalance visibility, and runbooks for the two or three failures that actually happen.
Data movement into the warehouse
Change data capture from operational databases and connectors into the lake, with exactly-once semantics where duplicates would corrupt reporting.
Security and multi-tenancy
SASL or mTLS authentication, per-topic ACLs, and encryption in transit, so one team cannot read another team’s stream by accident.
Disaster recovery
Replication between clusters, a defined recovery point, and a drill that proves consumers survive a cluster failover.
How we work with Kafka
We refuse to let the first producer ship before topic naming, partitioning keys, and schema compatibility are agreed. Almost every Kafka incident we are called in to fix traces back to a key choice made casually in week one: ordering breaks the moment a message key is wrong.
Once contracts are in place, we treat lag as the primary product signal. Consumer lag per group, with alerting tied to business impact rather than raw thresholds, tells the team what is actually falling behind before customers notice.
Kafka in production: where it breaks
The same handful of problems show up on almost every engagement. Naming them early is cheaper than discovering them at 2am.
Partition counts set far too high
Hundreds of partitions per topic lengthen rebalances and stall consumers. We size from measured throughput and grow deliberately.
Missing or skewed message keys
Events for one entity land on different partitions, so ordering silently breaks. We choose keys per event type and document why.
Consumers without a dead-letter path
One malformed event blocks an entire partition forever. Dead-letter topics plus replay tooling keep the stream moving.
When to use Kafka
Order, payment, and inventory flows that must stay consistent across services.
Change data capture feeding a warehouse without nightly batch windows.
Telemetry backbones carrying product and infrastructure events.