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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.

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What you get back

  1. 1. Diagnosis What works, what is blocked, and why.
  2. 2. Recommendation Audit, advisory, sprint, or pause.
  3. 3. Scope Next action, boundaries, and timing.
// Kafka cluster health check
$ kafka-consumer-groups --bootstrap-server prod:9092 --describe --all-groups
✓ Brokers: 6 · Partitions: 1,284 · Replication: 3
✓ Consumer lag: 0 · Throughput: 48K msgs/sec
✓ Schema Registry: 214 schemas · Avro + Protobuf

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

CapabilityWhat We Deliver
Real-time data pipelinesKafka Connect source/sink connectors for CDC ingestion from PostgreSQL, MySQL, MongoDB, and S3
Stream processingKafka Streams and ksqlDB for stateful transformations, windowed aggregations, and real-time enrichment
Event-driven microservicesevent sourcing with compacted topics, CQRS patterns, and transactional outbox
Streaming analyticsreal-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.

Evidence

Deployments in this area

Kafka Isolation Forest

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%.

events_day: 2.4M
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Apache Kafka Apache Spark Streaming

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.

data_processing: Real-Time
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Engineering Intelligence

Related articles

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AI 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.

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Kafka 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.

Next Step

Discuss your Apache Kafka Engineering path

Send the system context, constraints, and pressure. A Principal Engineer reviews it and recommends the next step.

[ SUBMIT SPECS ] [ SEE OUR WORK ]

No SDRs. A Principal Engineer reviews every submission.