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Solution · Flink CDC

CDC at scale,under control.

Build, deploy, observe, and recover every Flink CDC pipeline through one production lifecycle.

The operating problem

Data movement is only half the job.

At scale, release, diagnosis, recovery, and governance become the real constraints.

01

Pipeline sprawl

Every new source, target, and team multiplies delivery and support paths.

RISK / INCONSISTENCY
02

Hidden failure context

Split metrics, logs, and recovery history slow diagnosis.

RISK / RECOVERY TIME
03

Operational ownership

Open-source components leave production standards and support ownership to the platform team.

RISK / PLATFORM LOAD
Realfuture for CDC

One operating model for every pipeline.

Keep Apache Flink CDC as the data-movement foundation. Add one enterprise lifecycle for delivery, control, and recovery.

Repeatable delivery

Move every CDC job through one development, validation, and deployment path.

Operational visibility

Keep metrics, logs, alerts, and history with the workload.

Controlled recovery

Standardize recovery and production changes without losing ownership or auditability.

Enterprise control

Apply access, audit, and production standards consistently as the estate grows.

Reference workflow

Evaluate the lifecycle, not a slide deck.

Test a representative source, target, and failure mode. Prove both initial delivery and day-two operations.

01 / CONNECT

Define the pipeline

Choose a representative source, target, schema, and daily volume.

02 / SHIP

Release consistently

Validate authoring, configuration, and deployment responsibilities.

03 / OPERATE

Observe production

Review runtime signals, alert context, and fleet-level oversight.

04 / RECOVER

Prove failure handling

Exercise recovery and operational change with clear ownership.

Source and sink support, schema evolution and delivery semantics vary by connector and version. Use a representative pipeline to validate the required behavior.

Review compatibility and evaluation requirements

Bring a CDC workload that reflects production.

We’ll turn job volume, data scale, system mix, recovery objectives, and deployment constraints into a practical POC.