Recently I’ve been using Starburst as a federated SQL and policy layer for analytics on datasets hosted in Google Cloud, and I’ve spent some time understanding why Starburst is increasingly being adopted by large enterprises as a key layer in modern analytics platforms.
Built on Trino, Starburst enables teams to query data across systems through one unified SQL access layer, reducing unnecessary ETL-based data movement while improving governance, consistency, and analytics agility. Its biggest architectural advantage is Federated Analytics:
query across systems without ETL moving everything first - Good slogan, but enterprises still do ETL / ELT. More realistically, reduce the need for ETL-based data movement.
Instead of copying data into a centralized warehouse, Starburst enables teams to query data in place across systems such as:
through one unified SQL access layer.
Modern analytics architecture increasingly looks like:
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Storage + Compute + Semantic / Federated Layer + AI / Visualization
Starburst sits squarely in that middle layer, acting as a **federated brain** that connects systems, governs access, and powers downstream analytics.
A typical GCP deployment:
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Oracle / PostgreSQL / BigQuery / Cloud Storage
↓
Starburst
↓
Looker / Tableau / Power BI / AI Agents / Python Analytics
Key benefits:
In short:
Starburst is becoming a critical federation and semantic layer in modern enterprise data architecture. (Starburst)