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If your data already lives as parquet files in object storage (a “data lake”), Erdo can query it where it is. You don’t copy anything into Erdo, and you don’t need a warehouse like BigQuery or Snowflake in the middle — Erdo reads the parquet directly with DuckDB over the object store. This is the right connector when you have a lake like:
Each top-level folder (events, orders, …) becomes a table you can query. Hive-partitioned columns like year/month/day are real, queryable columns too.

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What you’ll need

GCS

Connect the same way as BigQuery — either:
  • Sign in with Google (OAuth, read-only storage scope), or
  • paste a service-account JSON for an account with read access to the bucket (roles/storage.objectViewer).
No HMAC interoperability keys (which many orgs disable by policy).

S3 / S3-compatible

An access key id + secret with read access. Works with AWS S3 and S3-compatible stores (Cloudflare R2, MinIO, Backblaze B2, …) — supply the custom endpoint for those.
The credentials only need read access — the connector never writes to your lake.

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Connect it

1

Open Data → Connectors → Data Lake

Choose the Data Lake connector.
2

Enter your lake details

  • Provider — gcs or s3
  • Root — e.g. gs://your-bucket/warehouse or s3://your-bucket/warehouse
  • Credentials — Sign in with Google or paste a service-account JSON (GCS); or an access key + secret (S3)
  • Region / endpoint — for S3 / S3-compatible stores (optional)
3

Pick your tables

Erdo lists the tables it finds under the root, with their columns. Select the ones you want to make queryable — they become datasets just like any other connected source.
4

Query and build

Ask an agent a question, or build a page on top. Agents query your lake with SQL and reason over the results — the parquet never leaves your bucket.

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How queries run

Erdo runs each query in a sandboxed DuckDB that reads your parquet over the network using your credentials. A few things worth knowing:
  • In place, no copy. Data stays in your bucket; Erdo streams only what a query needs (parquet column + partition pruning keeps reads small).
  • Scoped to your tables. Agent-generated SQL can only read the tables you selected — it can’t be pointed at arbitrary paths, even within the same bucket.
  • Partition-aware. Filter on the Hive partition columns (year, month, …) and Erdo only scans the matching files.
Lake vs. warehouse. Connect a data lake when your source of truth is parquet files in object storage. If your data lives in a query engine (PostgreSQL, Snowflake, BigQuery, ClickHouse), connect that database instead — Erdo will query it natively.