30+ Database Connectors: How Semelabs Unifies Your Data Stack

Enterprise data is fragmented by design. Sales data lives in PostgreSQL. Finance runs on SQL Server. The data warehouse is Snowflake. Marketing analytics are in BigQuery. And somewhere, there’s a legacy Oracle instance no one wants to touch.

The traditional approach to unifying this data involves ETL pipelines, data lakes, and months of engineering work. But what if you could query all of it from a single interface — without moving a single row?

The Problem with Data Consolidation

Most enterprises spend 60–80% of their data engineering budget on data movement: extracting, transforming, and loading data from operational systems into centralized warehouses. This creates:

• Stale data — ETL pipelines run on schedules, so reports are always slightly behind
• Data duplication — The same data exists in multiple places, creating governance headaches
• Engineering overhead — Pipeline maintenance is a full-time job for multiple engineers
• Security risk — Every data copy is another surface to protect

Query in Place, Don’t Move

Semelabs takes a different approach. Instead of moving data to a central location, we connect directly to your databases where they already live. With 30+ native connectors, Semelabs supports:

Warehouses & Lakes

• Snowflake — Full support including roles, warehouses, and schemas
• Google BigQuery — Native integration with Google Cloud IAM
• Amazon Redshift — Direct connection with IAM authentication
• Databricks — Unity Catalog support

Relational Databases

• PostgreSQL — The most popular connector, with read-only enforced connections
• MySQL — Full support for cloud-managed and self-hosted instances
• SQL Server — Azure SQL, AWS RDS, or on-premises
• Oracle — Including Oracle Autonomous Database

Analytical Engines

• ClickHouse — For high-performance analytical queries
• Apache Hive — For Hadoop-based data lakes
• Presto / Trino — For federated query engines
• DuckDB — For embedded analytics

How It Works

1. Connect — Provide read-only credentials. Semelabs introspects your schema automatically.
2. Configure — Set row-level security policies, column masks, and user permissions.
3. Query — Business users ask questions in plain English. The AI agent generates governed SQL and executes it against the correct database.

All connections are read-only. Your data never moves, is never copied, and is never stored by Semelabs.

One Interface, Every Database

The value isn’t just connecting to databases — it’s giving every business user a single, governed interface to query across all of them. A VP of Sales shouldn’t need to know whether revenue data is in Snowflake or PostgreSQL. They should just be able to ask: “What was Q3 revenue by region?”

The future of enterprise data isn’t a bigger warehouse. It’s a smarter interface to the warehouses you already have.