Apache Kafka Engineering
Production Kafka clusters processing millions of events per second. We architect real-time streaming pipelines, event-driven microservices, and CDC infrastructure with exactly-once semantics, Schema Registry governance, and zero-downtime upgrades.
What happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
Real-Time Streaming Infrastructure
We design and operate Apache Kafka clusters that serve as the central nervous system for distributed architectures — from event sourcing to CDC to streaming analytics.
What We Build
| Capability | What We Deliver |
|---|---|
| Real-time data pipelines | Kafka Connect source/sink connectors for CDC ingestion from PostgreSQL, MySQL, MongoDB, and S3 |
| Stream processing | Kafka Streams and ksqlDB for stateful transformations, windowed aggregations, and real-time enrichment |
| Event-driven microservices | event sourcing with compacted topics, CQRS patterns, and transactional outbox |
| Streaming analytics | real-time dashboards and anomaly detection on unbounded event streams |
Engineering Standards
- Exactly-once semantics with idempotent producers and transactional consumers
- Partition strategy tuned for throughput and ordering guarantees per domain
- Schema evolution governed by Confluent Schema Registry (Avro/Protobuf, compatibility modes)
- Monitoring stack: Prometheus + Grafana + Burrow for consumer lag tracking
- Multi-datacenter replication with MirrorMaker 2 for disaster recovery
- Zero-downtime rolling upgrades and broker decommissioning procedures
Depth of Practice
We maintain 15+ published articles on Kafka architecture, Kafka Streams internals, ksqlDB patterns, and production operations on the ActiveWizards blog. Our engineers operate Kafka clusters handling sustained throughput across financial services, healthcare, and e-commerce domains.
Deployments in this area
Real-time anomaly detection processing 2.4M events/day with 70% fewer false positives
How we built a real-time anomaly detection pipeline processing 2.4M events/day using Kafka, Isolation Forest, and foundation models. False positive rate reduced from 68% to under 20%.
Real-Time IoT Analytics Platform for Smart Agriculture
We built a real-time streaming analytics platform for an AgriTech startup, processing live GPS data from farming equipment to track field coverage, calculate equipment utilization, and deliver dynamic ETAs to mobile devices.
Related articles
Building the Feature Store on Kafka and Spark: Real-Time and Batch Feature Serving Architecture
How to build a production feature store with Kafka for online features and Spark for batch, covering Feast integration, Redis/Delta serving, and freshness SLAs.
Data EngineeringKafka Connect for AI Data Ingestion: Source Connectors, Schema Registry, and Pipeline Reliability
How Kafka Connect source connectors, Schema Registry, dead letter queues, and SMTs build reliable AI data ingestion pipelines — with examples.
AI AgentsStreaming RAG: Real-Time Retrieval for Agents That Can't Wait
How to build a low-latency RAG pipeline that retrieves from live Kafka streams — architecture patterns, ingestion trade-offs, and failure modes from production.
Discuss your Apache Kafka Engineering path
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.