Apache Kafka Engineering
Production Kafka clusters for real-time streaming pipelines, event-driven microservices, and CDC infrastructure with delivery semantics, Schema Registry governance, and upgrade discipline.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
Real-Time Streaming Infrastructure
We design and operate Apache Kafka clusters that serve as the event backbone for distributed architectures: event sourcing, CDC, streaming analytics, and operational data movement.
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
| Standard | What It Protects |
|---|---|
| Delivery semantics designed per workload | Producers, consumers, retries, and transactions match the actual consistency need |
| Partition strategy tuned by domain | Throughput and ordering trade-offs are explicit |
| Schema evolution with Schema Registry | Producers and consumers change without silent contract drift |
| Consumer lag monitoring | Backpressure and stalled consumers are visible before incidents reach product teams |
| Replication and recovery design | Disaster recovery behavior is planned before failure |
| Rolling upgrade procedures | Broker changes happen through a controlled operations path |
Depth of Practice
We maintain a published Kafka architecture library covering Kafka Streams internals, ksqlDB patterns, and production operations on the ActiveWizards blog. Our engineers operate Kafka clusters for financial services, healthcare, and e-commerce data workflows.
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
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AI AgentsKafka for AI Agents: A Real-Time Agent Architecture
A practical architecture for using Kafka with AI agents, including Kafka Streams for feature engineering, real-time context, and production agent workflows.
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