Apache Flink
Stream processing framework for stateful real-time computation
https://flink.apache.orgWhat It Is
Apache Flink is a distributed stream processing framework for stateful computations over unbounded and bounded data streams. Unlike batch-first systems, Flink was designed from the ground up for stream processing — offering true event-time semantics, exactly-once state consistency, and millisecond-level latency.
How We Use It
We use Flink for real-time processing scenarios that require stateful computation, event-time handling, and low-latency guarantees. Typically paired with Kafka as the event source, Flink handles complex event processing, real-time aggregation, and streaming ETL.
Our Expertise
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✓Streaming Architecture
We design end-to-end streaming architectures with Flink: Kafka for ingestion, Flink for processing, and analytical databases for serving.
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✓Event-Time Processing
We implement watermark strategies, late event handling, and allowed lateness windows for correct out-of-order processing.
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✓Windowed Aggregations
We build real-time aggregations using tumbling, sliding, session, and custom windows.
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✓Kafka-Flink Integration
We configure the Flink Kafka connector for exactly-once guarantees with checkpointing.
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✓Stateful Processing
We develop stateful applications using keyed state and operator state with RocksDB backends.
Typical Use Cases
Anomaly Detection
Real-time anomaly detection and alerting on streaming data.
Continuous Aggregation
Live dashboards powered by continuously aggregated event streams.
Streaming ETL
Transforming and enriching data in flight before landing in analytical stores.
Complex Event Processing
Pattern matching and fraud detection on real-time event streams.
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