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 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.
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
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How to build a low-latency RAG pipeline that retrieves from live Kafka streams — architecture patterns, ingestion trade-offs, and failure modes from production.
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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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