Pipeline sprawl
Every new source, target, and team multiplies delivery and support paths.
RISK / INCONSISTENCYSolution · Flink CDC
CDC at scale,under control.
Connect source databases to Paimon, Fluss, Doris, StarRocks and other supported targets. Develop CDC pipelines with AI assistance, validation, table routing, transformations and configurable schema evolution.
The operating problem
Data movement is only half the job.
At scale, release, diagnosis, recovery, and governance become the real constraints.
Hidden failure context
Split metrics, logs, and recovery history slow diagnosis.
RISK / RECOVERY TIMEOperational ownership
Open-source components leave production standards and support ownership to the platform team.
RISK / PLATFORM LOADRealfuture for CDC
One operating model for every pipeline.
Connect ingestion to the rest of your lakehouse workflow: catalog discovery, AI-assisted development, source-target validation and production operations.
Develop with context
Generate configuration from connector templates or with AI assistance, then review and validate it before publishing.
Transform and route
Map tables and fields, filter rows and transform values for downstream requirements.
Schema evolution
Choose how supported schema changes are applied, according to pipeline policy and source-target capabilities.
Operate and investigate
Inspect status, metrics, logs and checkpoints, with read-only AI diagnosis and controlled recovery operations.
Reference workflow
Follow a pipeline from source to target.
Test a representative source, target, and failure mode. Prove both initial delivery and day-two operations.
Define the pipeline
Select source and target connectors, generate a template and configure table scope.
Release consistently
Review AI or authored configuration, routing and transformations, then validate and publish.
Observe production
Review runtime signals, alert context, and fleet-level oversight.
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 requirementsBring a CDC workload that reflects production.
We’ll turn job volume, data scale, system mix, recovery objectives, and deployment constraints into a practical POC.