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Connection flows explained

Every connector in the wizard uses one of a small set of connection flows — the form you fill in. Once you’ve connected one host/port database, you’ve effectively learned them all. This page explains each flow once, so the per-connector pages can stay focused on credentials and permissions.

The Data Sources page listing connected sources with their type and status.

Host / port databases

Section titled “Host / port databases”

The standard relational database form: host, port, database name, username, password, plus a TLS toggle and an optional Trust self-signed certificates switch.

  • Prefilled ports — The wizard automatically fills in the default port for the selected engine (e.g. 5432 for PostgreSQL, 3306 for MySQL, 1433 for SQL Server, 8123 for ClickHouse, 8080 for Trino, 1521 for Oracle).
  • Wire compatibility — Many modern engines are protocol-compatible with a major database standard:
    • PostgreSQL-compatible: TimescaleDB, CockroachDB, Greenplum, YugabyteDB, Materialize, RisingWave, Neon, Supabase, AlloyDB.
    • MySQL-compatible: SingleStore, TiDB, PlanetScale, StarRocks, Doris, OceanBase.
    • SQL Server (TDS)-compatible: Azure Synapse, Microsoft Fabric Warehouse.
  • Engine-specific fields — Special engines include dedicated fields, such as the Controller URI for Apache Pinot schema and table discovery.

Cloud warehouses

Section titled “Cloud warehouses”

Cloud data warehouses use dedicated authentication forms matching each platform’s security model:

  • Snowflake — Account identifier (org-account), virtual warehouse, database, username, password, and optional role.
  • Databricks — Server hostname, SQL Warehouse HTTP path, and a Personal Access Token (PAT).
  • Google BigQuery — GCP Project ID, Service Account JSON private key, default dataset, and optional processing location (e.g. US, EU).
  • Amazon Athena — AWS Region, IAM Access Key ID, Secret Access Key, S3 query result staging output path (s3://bucket/path/), and optional Athena Workgroup.
  • DuckDB & MotherDuck — File path or DSN (:memory:, local.duckdb, or md:<database>) with an optional MotherDuck service token.

Object stores

Section titled “Object stores”

Amazon S3 (including S3-compatible stores like MinIO and Cloudflare R2), Azure Blob Storage, Azure Data Lake Storage Gen2, and Google Cloud Storage connect directly to your storage buckets:

  • Credentials by cloud:
    • Amazon S3 / MinIO: AWS Region, Access Key ID, Secret Access Key, Bucket name, and optional custom endpoint URL for S3-compatible storage.
    • Azure Blob / ADLS Gen2: Storage account name, Container name, and either a Shared Access Signature (SAS) token or Storage Account Key.
    • Google Cloud Storage: Bucket name and GCP Service Account JSON key.

The Object Browser & dataset imports

Section titled “The Object Browser & dataset imports”

Connecting an object store verifies bucket accessibility. Once connected, open the Browse Objects dialog:

  • List with prefixes — Filter bucket contents using a prefix (e.g. telemetry/2026-08/ or events/).
  • Single-object import — Select an individual Parquet, CSV, or JSON file to decode and import as a managed workspace dataset.
  • Partition prefix import — Import an entire multi-part directory (part-00000.parquet, part-00001.parquet, …) as one unified dataset rather than creating dozens of separate tables.
  • Scheduled pipelines — For continuous ingestion of incoming bucket files, configure a Data Pipeline with glob patterns.

File uploads

Section titled “File uploads”

Upload files directly from your computer without needing external servers:

  • CSV (.csv) — Ingest tabular data into a staged table with automatic delimiter and type inference.
  • Excel (.xlsx) — Multi-sheet workbook support; each sheet imports as an independent queryable table.
  • SQLite (.sqlite, .db) — Upload a local SQLite database file; all contained tables are staged and queryable immediately.
  • Parquet (.parquet) — Upload column-oriented binary data for high-speed analytical staging.
  • JSON / NDJSON (.json, .ndjson) — Upload JSON arrays or newline-delimited JSON records with an optional Records path (e.g. data.items) to unnest nested arrays.

Re-uploading the file on an existing source updates the staged dataset in place.

API pulls

Section titled “API pulls”
  • REST API — Connect to any JSON API endpoint with custom request headers (e.g. Authorization: Bearer <token>), HTTP parameters, and a JSON records path. Configure automatic scheduled polling or signed webhook triggers.
  • Google Sheets — Share your spreadsheet with the DataSquares service account email and provide the sheet URL; every sheet tab imports as a table.

Import to DataSquares (Warehouse Datasets)

Section titled “Import to DataSquares (Warehouse Datasets)”

For any connected database or warehouse, you can use Import to DataSquares (gated on the Warehouse engine) to replicate tables or query results directly into your high-performance workspace warehouse for blazing-fast cached queries and offline analytics.

Credentials & security

Section titled “Credentials & security”
  • Encryption at rest — All credentials (passwords, tokens, service account keys) are encrypted using AES-256 before storage and never leave the server.
  • Read-only principle — DataSquares only runs read queries against your sources. Always provision dedicated database users with SELECT permissions.
  • TLS enforcement — Connections enable TLS by default. Self-signed certificates can be explicitly allowed via the “Trust self-signed certificate” option when operating behind internal corporate CAs.
Section titled “Related”
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