Connect your data

DataSquares connects to your data through a guided connection wizard — pick a connector below for its step-by-step setup guide.

How DataSquares connects

DataSquares supports four primary connection methods tailored to different data architectures:

Direct query databases & warehouses — Live SQL execution against relational databases (PostgreSQL, MySQL, SQL Server, Oracle), cloud warehouses (Snowflake, BigQuery, Databricks, Athena), and analytics engines (ClickHouse, Trino, DuckDB). Data stays in your database.
Object store browsing & ingestion — Connect buckets in Amazon S3, Azure Blob / ADLS Gen2, or Google Cloud Storage. Browse objects and import single files or multi-part partition prefixes (Parquet, JSON, CSV) into managed workspace datasets.
File uploads — Upload local CSV, Excel (.xlsx), SQLite, Parquet, and JSON/NDJSON files directly into workspace staging for instant querying.
API pulls — Ingest data from REST APIs and Google Sheets with scheduled atomic refresh and HMAC-signed webhook sync triggers.

Most connectors require only read access — grant the connection user read-only permissions and DataSquares takes care of the rest. Credentials are encrypted at rest and never leave the server.

Connector catalog

Search or filter by category to find your data source:

Data Warehouses

Cloud Storage & Lakes

Files & Apps

Analytics & Marketing

Developer & APIs

Streaming

Next steps

Connection flows explained — Host/port forms, cloud warehouse auth, file ingestion, and object stores.
Refreshing data & schemas — Atomic refresh, automated schedules, run history, and schema discovery.
Troubleshooting connections — Resolving network timeouts, TLS certificates, credentials, and permissions.