Flink specialists for your dedicated team.

Where Flink fits in a long-term product team.

Event-time processing is the capability that distinguishes Flink. Events arrive late and out of order in every real system, and Flink handles that with watermarks and windows based on when something happened. Systems built on processing time quietly produce wrong answers whenever the network is slow.

Managed state is the other half and the harder operational half. Flink holds application state and checkpoints it consistently, which is what makes exactly-once possible, and it means state size, checkpoint duration and savepoint compatibility decide whether a job can be upgraded without losing what it knows.

What your dedicated team does with Flink.

Streaming systems are considerably harder to operate than batch ones. A failed batch job is rerun; a failed streaming job has state, position and downstream consumers to reconcile, so recovery has to be designed in advance.

That operational depth is exactly what accumulates in a standing assignment and disappears when capacity rotates. What that commitment covers is set out in how the monthly fee is built.

Common Flink use cases.

• Correct results with late data Event-time windows and watermarks, so out-of-order arrival does not corrupt the output.
• Stateful stream processing Aggregations and joins over streams with state that survives restarts.
• Upgrades without losing state Savepoint compatibility planned, so a new version resumes where the old one stopped.

Adding Flink skills to your team.

Stream processing capacity is assigned in a dedicated data engineering team, with recovery and upgrade paths designed before the first job goes live.

You can add Flink capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.