Modern cloud transformation strategies all push the same narrative: centralize your data, build a lakehouse, and let AI do the rest.
The reality?
Data is still scattered.
Cloud costs are exploding.
Latency still kills insights.
And “migrating everything” is rarely practical — or even possible.
Enter Starburst.
A federated query engine built for the AI era — not the batch reporting past.
Starburst doesn’t try to replace your data stack. It sits on top of it, connecting everything, and delivering fast, secure, federated analytics across clouds, formats, and sources — without lifting and shifting.
Most enterprises now operate in a fragmented analytics reality:
Customer data in Snowflake
Clickstream in S3
Orders in PostgreSQL
Inventory in Oracle
Historical logs in Hadoop
AI features in Delta Lake
Compliance logs in Azure Blob
Each cloud migration, tool adoption, or acquisition only compounds the mess.
Starburst doesn’t ask you to fix this. It gives you a federated SQL engine that makes it usable — immediately.
| Capability | Strategic Value |
|---|---|
| Trino-Based Engine | Open-source, massively parallel query engine (formerly PrestoSQL), built for speed and federation |
| Starburst Galaxy | Fully managed SaaS platform with enterprise connectors, security, caching, autoscaling |
| Federated Query | Query across S3, Snowflake, BigQuery, Databricks, Postgres, Hive, Delta Lake, Oracle — simultaneously |
| Built-in Caching & Acceleration | Smart query acceleration via materialized views and result caching |
| Fine-Grained Security | RBAC, masking, row-level filtering, OAuth/SAML/LDAP integrations |
| Cost Governance | Pushdown, audit, and optimization features help query without blowing up your Snowflake/GCP bills |
| Data Products Layer | Package and publish reusable, governed data sets for self-service analytics or AI pipelines |
It’s a query fabric — not a storage product. And in the AI era, that’s exactly what many enterprises need.
While most associate Starburst with fast SQL, its real value is enabling AI and analytics at cloud scale — without rearchitecting everything.
Use cases include:
RAG Pipelines
Retrieve structured facts from distributed sources to ground LLMs
(e.g., query contracts in Hive + recent transactions in Snowflake)
Cost-Effective Exploration
Analysts can query cloud data without paying to ingest/move it first
(e.g., test model features directly across Parquet, Delta, and SQL)
Multi-Cloud Federated Analytics
One query spans Azure + AWS + on-prem without flattening infrastructure
Data Productization
Package curated, documented, and governed data sets for downstream consumption by analysts, models, or agents
Starburst doesn't care where your data lives.
It just makes it work — securely, fast, and at scale.
Months 1–2: Discovery & Justification
Map high-latency pipelines, duplicated ETL, and cost-heavy data movement
Identify target data sources: cloud storage, legacy DBs, cloud warehouses
Stand up Starburst Galaxy and run pilot queries across multiple sources
Months 3–5: Consolidate & Accelerate
Replace brittle pipelines with federated SQL views
Enable access control and query auditing
Introduce caching layers for high-traffic use cases
Months 6–9: AI Pipeline Enablement
Feed Starburst queries into feature stores, RAG workflows, or LLM-grounding APIs
Combine unstructured + structured sources in hybrid pipelines
Tag and expose curated data sets as reusable products
Months 10–12: Governance & Optimization
Implement data mesh / domain ownership model
Monitor query patterns and optimize cost with pushdown + filters
Train teams to consume Starburst products via BI or code
With Starburst, success is measured by access, speed, and savings — not storage volume:
Time-to-insight across fragmented sources
Data movement reduction (volume + cost)
Query latency on large, federated joins
Analyst adoption of federated datasets
Cost delta vs loading everything into Snowflake/BigQuery
Model performance in RAG pipelines with federated grounding
✅ Enterprises with data spread across clouds, lakes, warehouses, and legacy stores
✅ Platform teams tired of building and maintaining brittle ETL pipelines
✅ Data product owners building self-service layers for AI, BI, or automation
✅ AI/ML teams needing structured grounding context for LLMs
✅ CDOs aiming to de-risk replatforming while improving access
Everyone’s telling you to consolidate, replicate, migrate, or replatform.
Starburst gives you another option: query it where it lives — securely, intelligently, and at scale.
In an AI-driven world, the speed of insight depends less on where your data is — and more on whether you can use it, trust it, and integrate it in real time.
That’s why Starburst matters.
Not as another database — but as the query layer your cloud migration strategy forgot.
