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 happens next
- 1. Context We review the situation and constraints.
- 2. Fit We recommend an appropriate next step.
- 3. Scope If relevant, we discuss scope.
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.
Engineering evidence
Real-time anomaly detection processing 2.4M events/day with 70% fewer false positives
Mid-Market Healthcare Provider
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
Client engagement · anonymized
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
Streaming 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.
AI AgentsAI Agents for Real-Time Anomaly Detection: Kafka and AIOps Architecture
A practical AIOps architecture for real-time anomaly detection using Kafka and AI agents, with automated investigation, tool-based triage, and incident report generation.
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.
Discuss your Apache Kafka Engineering path
Tell us about your system, the decision ahead, and the constraints. We will review the context and recommend the next step.
Direct contact with a principal engineer.