Apache Flink Engineering
Stateful stream processing with Apache Flink. Unified batch and streaming pipelines, event-time semantics, and real-time analytics processing millions of events per second with exactly-once guarantees.
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.
What We Build with Flink
| Capability | What We Deliver |
|---|---|
| Stateful stream processing | Event-driven applications on the DataStream API with managed state, queryable state backends, and automatic state migration across job upgrades |
| Unified batch and streaming | Single Flink SQL codebase for both real-time dashboards and historical batch reprocessing, eliminating dual-pipeline maintenance |
| Real-time analytics | Windowed aggregations, pattern detection with Flink CEP, and continuous ETL feeding downstream warehouses and feature stores |
| Change Data Capture | Flink CDC connectors for MySQL, PostgreSQL, and MongoDB with schema evolution tracking and zero-downtime migrations |
Engineering Standards
- Exactly-once semantics via aligned and unaligned checkpointing with incremental RocksDB state backend
- Event-time processing with custom watermark strategies for out-of-order and late-arriving data
- Savepoint-driven deployments for zero-downtime upgrades and state schema evolution
- Backpressure monitoring, flame graphs, and per-operator metrics exported to Prometheus
- Infrastructure-as-code: Flink on Kubernetes via flink-kubernetes-operator with autoscaling TaskManagers
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Sub-second latency streaming with complex stateful processing | Apache Flink — this page |
| Batch ETL at scale, ML pipelines, lakehouse architecture | Apache Spark — better batch ecosystem |
| Simple stream transformations without state management | Kafka Streams — lighter-weight, no separate cluster |
| CDC from databases to downstream systems | Flink CDC or Kafka Connect + Debezium — depends on transformation needs |
| Real-time OLAP queries on streaming data | Apache Druid — query layer, not processing |
Depth of Practice
We maintain published articles on Flink architecture, stateful stream processing, and real-time analytics on the ActiveWizards blog. Our engineers operate Flink clusters processing millions of events per second across financial services, IoT telemetry, and real-time recommendation systems.
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 Flink 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.