Sound familiar?
- ▸ Primary-key model upserts blowing up memory on BE nodes — the table fits the use case but you're hitting OOM during compaction, and the docs don't cover the working-set sizing that actually prevents it.
- ▸ Migrating from ClickHouse or Doris and need an honest view of what changes — the wire-protocol compatibility looks good on paper, but the production difference is in JOIN strategy, MV semantics, and ingestion architecture.
- ▸ CelerData Cloud vs self-managed — your finance team wants TCO numbers before the next renewal, and the StarRocks-Inc. comparison page is unsurprisingly optimistic about the cloud path.
JusDB StarRocks consultants give you the written decision document with real numbers from real deployments. Book an architecture review →
Strategic advisory — not execution
StarRocks Consulting Services
In short: StarRocks consulting is strategic advisory for sub-second OLAP — schema-model selection (primary-key vs aggregate vs duplicate vs unique), ingestion-pipeline design (Stream/Routine/Broker Load, Flink), materialized-view strategy, FE/BE/CN topology sizing, and Iceberg lakehouse integration. You need it when primary-key upserts blow up memory, a ClickHouse/Doris migration looms, or a CelerData-vs-self-managed TCO call is due.
Schema-model selection, ingestion-pipeline design, materialized-view strategy, and lakehouse integration for StarRocks and CelerData — delivered as written recommendations and trade-off documentation. See the StarRocks hub for the broader services overview.
Advisory Coverage
What our StarRocks consulting covers
Each deliverable is a written decision document, sized topology proposal, or costed trade-off analysis — never a Slack-thread recommendation.
Schema Model Selection
Primary-key vs aggregate vs duplicate vs unique model — chosen based on your actual write pattern (upsert vs append), read pattern (scan vs lookup), and storage budget.
Ingestion Pipeline Design
Stream Load, Routine Load (Kafka), Broker Load, Flink Connector, Spark Connector — chosen by freshness SLA and existing infrastructure, not by documentation order.
FE/BE/CN Topology
Frontend (metadata + planner), Backend (storage + executor), Compute Node (shared-data lakehouse architecture) — sized for query concurrency, scan size, and data freshness.
Materialized View Strategy
Async MV vs sync MV vs query-time rollup decisions based on slow-query analysis, not blanket pre-aggregation that bloats storage and slows ingestion.
Lakehouse Integration
Iceberg, Hudi, Delta Lake external-catalog setup, Iceberg-as-source vs Iceberg-as-cold-tier strategy, metadata-only vs full-load patterns — for hybrid lakehouse-warehouse architectures.
Capacity & Cost Planning
Growth modeling, shared-data vs shared-nothing decision (StarRocks 3.x onwards), elastic compute sizing, S3/OSS storage tiering, reserved-instance modeling for self-managed.
CelerData vs Self-Managed
CelerData Cloud (managed StarRocks) vs self-managed-on-EC2-or-K8s decision modeling, including the operational-burden offset and the feature-availability delta.
Engagement Shapes
How a StarRocks consulting engagement is shaped
Four engagement shapes, each with a different deliverable. Consulting owns the decision phase; once the plan is signed off, execution moves to StarRocks migration and performance tuning.
Architecture Review
Engine Decision
Migration Strategy
Greenfield Design
Decision Matrix
Table model matrix — when each one fits
Rough decision shape before a real engagement. Actual recommendation depends on your workload.
| Table model | Fits when | Avoid when |
|---|---|---|
| Primary-Key model | Upsert-heavy CDC streams, slowly-changing dimensions, real-time updates with point lookups. | Memory budget is tight and write QPS is low — duplicate model is simpler. |
| Aggregate model | Pre-aggregated metrics, time-series rollups, you never need raw rows after ingestion. | You need raw-row drill-downs or ad-hoc filters — aggregation collapses what you can ask. |
| Duplicate model | Append-only fact tables, range-scan analytics, log analytics with no dedup requirement. | Source emits duplicates and you need exactly-once semantics — use primary-key. |
| Unique model | Upsert without the primary-key index memory cost, low write QPS. | High write QPS — primary-key model is strictly better since StarRocks 3.x. |
FAQ
StarRocks consulting — common questions
Ready to make the call on StarRocks?
Book a 30-minute scoping call. We'll tell you which engagement shape fits and what the deliverable will look like — before you commit to a statement of work.
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