Batch is fine until the business needs now. We build real-time ingestion and stream processing on Kafka, Kinesis and MSK — exactly-once, observable, and built to scale.
Streaming done right is boring: data arrives, gets processed once, and shows up where it's needed.
High-throughput, durable event capture from apps, devices and change data capture — with backpressure handled.
Windowed aggregations, joins and enrichment with Flink or Spark Structured Streaming — exactly-once where it counts.
Stream changes out of operational databases without batch loads — keeping the lakehouse fresh by the second.
Schema registries, dead-letter queues, lag alerts and replay — so a bad message never silently corrupts the stream.
Streaming systems fail in subtle ways: silent lag, duplicate events, poison messages. We engineer for those up front with schema enforcement, exactly-once semantics, and replayable history.
Tell us what needs to be real-time. We'll design a streaming pipeline that's fast, exactly-once, and observable.
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