Apache Flink Consulting & Support for Enterprises

Our Flink consulting covers stateful stream processing architecture, event-time and watermark strategy design, migration from Spark Streaming or batch pipelines, CDC pipeline development, and cluster capacity planning. Every engagement is staffed by a named senior engineer with production Flink experience.

Flink is a true streaming engine with sub-second latency and native event-time processing, while Structured Streaming uses a micro-batch model that trades latency for simplicity. If your requirement is millisecond-level responses or complex event-time correctness, we typically recommend Flink; for most batch-adjacent workloads, Spark remains simpler to operate.

Yes — this is one of our most common Flink engagements. We start with the checkpoint duration and alignment metrics to isolate whether the bottleneck is state size, a slow operator, or backpressure from a downstream sink, then tune state backend configuration and parallelism until checkpoints complete reliably.

Yes. We design change data capture pipelines using Flink CDC connectors against source databases like PostgreSQL, MySQL, and MongoDB, including schema evolution handling and exactly-once delivery to downstream sinks like Kafka or a lakehouse table.

It depends on your isolation and resource-sharing requirements. We assess your workload mix and recommend the mode that matches — application mode is typically our default recommendation for production Kubernetes deployments because it isolates job failures cleanly.

We start with the checkpoint history and task manager logs to isolate whether the failure is a state issue, a data skew problem, or a resource constraint, then move to remediation using the last successful savepoint. Named engineers stay engaged until the topology is fully recovered.