Batch and real-time analytics without competing for one engine
Velocity Lakehouse combines open tables with a ClickHouse hot tier and separate transformation capacity. Plan how batch, interactive and existing warehouse work will be divided.
Relevant capabilities
- Iceberg, Trino and ClickHouse in one stack
- Separate transformation and interactive engines
- LakeGate policy and query routing
How should you compare the options?
- Which data belongs in the hot tier versus open tables?
- What freshness and concurrency does each consumer need?
- Which warehouse workloads should remain where they are?
What should you bring?
- Batch and interactive query examples
- Freshness targets and data volumes
- Existing warehouse dependencies
What does a practical first workflow look like?
- Classify workloads by freshness, latency and retention.
- Pilot one dataset through batch and hot-tier paths.
- Validate routing, reconciliation and cost before consolidating systems.
What should you check before expanding?
- Reconcile hot-tier and table results.
- Check interactive latency while batch work runs.
- Document replay and recovery for each tier.
How should you evaluate the cost?
Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.
What needs checking before switching?
AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.