Evaluate Realfuture
A useful evaluation follows one streaming lakehouse workload from its source data to production operations. Connect a catalog, develop with AI assistance where useful, publish a pipeline and inspect its results. Agree on the software and environment needed for the proof of concept.
1. Choose the workload
Describe its purpose, source and target systems, current component versions and the difficulty you want to address. Include the relevant catalogs, AI tasks and Paimon, Fluss, Doris or StarRocks workflows. Use representative test data. Do not send production credentials, personal data or confidential payloads through the website form.
2. Agree on the software and environment
Confirm the product release and component combination, installation responsibilities, network access, licensing scope and data boundaries. Obtain installation instructions for that exact release from the team.
3. Define success before running tests
| Area | Example evidence |
|---|---|
| Catalogs | Schemas and objects can be discovered and reused by the intended workspace roles |
| AI assistance | Generated SQL/CDC/ETL is reviewed and validated; diagnosis cites relevant job evidence |
| Development | A representative job can be authored, validated and deployed by the intended role |
| Operations | The team can find status, metrics, logs and the history needed to investigate a failure |
| Data integration | Snapshot, incremental changes and required schema changes produce the expected target data |
| Lakehouse and analytics | Target tables can be inspected and the required queries and maintenance actions verified |
| Recovery | A defined failure can be recovered from with the expected state and data correctness |
| Multi-cluster deployment | Jobs can be published and observed on the required YARN and Kubernetes targets from one platform |
| Performance | Throughput, latency and resource use measured on an agreed workload and baseline |
| Delivery | Responsibilities, support coverage and any additional work are recorded |
For performance comparisons, keep hardware, input, job logic and measurement windows comparable. Separate steady-state behavior, catch-up and recovery results. Resource scaling is not by itself proof of a change in processing throughput.
For a whole-database CDC evaluation, include more than one table, a filtering or mapping rule and a required schema evolution scenario. Compare both target schemas and data. If multiple clusters are a core requirement, include at least two target environments in the evaluation.
4. Review the result
Record what passed, what is conditional and what remains unresolved. Agree on production sizing, rollout, rollback and the software and service scope before proceeding.
Book a product demo or read about enterprise support.