Data
Data Sources are the connections that make your data available for AI-assisted analysis in Lakehousecat. Once a data source is connected and its semantic layer is extracted, the AI can query it in natural language, generate charts, and answer analytical questions.
Security and Credentials
All connection information — URIs, access keys, passwords — is encrypted at rest and never stored in plaintext. Lakehousecat operates in read-only mode: it loads data for internal processing but never writes back to your data source.
When configuring connections, apply the principle of least privilege — grant only the read permissions required. This limits exposure in the event of a misconfiguration or breach.
Supported Datasource Types
Lakehousecat connects to tabular data across a range of source systems:
| Category | Types |
|---|---|
| Relational Databases | PostgreSQL, MySQL, Microsoft SQL Server, Oracle Database |
| Cloud Data Warehouses | Google BigQuery, Amazon Redshift, Amazon Athena, Databricks |
| Analytical & Embedded | ClickHouse, DuckDB |
| NoSQL | MongoDB, Redis |
| Data Lake Table Formats | Delta Lake, Apache Hudi, Apache Iceberg |
| Query Engines & OLAP | Apache Drill, Apache Druid, Apache Hive, Trino |
| Object Storage | S3 File Storage |
| File Upload | CSV, Excel |
| Time Dimension | Timeline |
The set of supported connector types continues to grow. Additional types may be added in future releases.
SQLite is supported for local development and evaluation environments. It is not intended for production deployments.
Data Source Editor
After creating a data source, the editor provides tabs for detailed configuration:
| Tab | Purpose |
|---|---|
| General | Update name, connection settings, description, and status flags |
| Filters | Control which schemas, tables, and columns are included in the semantic layer |
| Descriptions | Review and edit semantic metadata — table and column descriptions, synonyms, data types |
| Hierarchies | Define and manage dimensional hierarchies for grouped analysis |
| Suggestions | Manage AI-generated example prompts tied to this data source |
| Operations | Trigger and schedule semantic extraction jobs |
Next Steps
Refer to the Guides → Data section for step-by-step instructions on creating and configuring data sources, and to Advanced Modeling for building multi-source analytical models.