Use an External Lakehouse
A project’s tables normally live in the built-in PlaidCloud Lakehouse. Instead, you can point a project at your own Snowflake or Databricks lakehouse as its data backend. Workflows then read and write project tables directly in your warehouse — the data stays in your account, under your governance and billing, while PlaidCloud drives the transformations, workflows, and dashboards over it.
Register the Lakehouse Connection
Section titled “Register the Lakehouse Connection”First create a connection to your lakehouse from Tools > Connections. This is an ordinary connection — the fields differ by provider.
Snowflake
Section titled “Snowflake”Choose Snowflake as the connection type and provide:
| Field | What to enter |
|---|---|
| Account (server) | Your Snowflake account identifier or host. |
| Warehouse | The compute warehouse PlaidCloud should run under. |
| Database | The database that holds the project’s schema. |
| Schema | The schema PlaidCloud reads and writes tables in. |
| Role | The Snowflake role PlaidCloud acts as. |
| User | The Snowflake user for the key pair. |
| Private key | The PEM private key for key-pair authentication. |
PlaidCloud authenticates to Snowflake with a key pair. Generate a key pair, assign the public key to the Snowflake user, and paste the private key into the connection. The role you give PlaidCloud must be able to create, read, and write tables in the target database and schema.
See the Snowflake connector reference for the full field list.
Databricks
Section titled “Databricks”Choose Databricks as the connection type and provide:
| Field | What to enter |
|---|---|
| Workspace host | Your Databricks workspace URL. |
| HTTP path | The HTTP path of the SQL warehouse PlaidCloud should run against. |
| Personal access token | A Databricks PAT for authentication. |
| Catalog | The Unity Catalog catalog that holds the project’s schema. |
| Schema | The schema PlaidCloud reads and writes tables in. |
PlaidCloud authenticates to Databricks with a personal access token and runs queries against a SQL warehouse — you must supply its HTTP path. Databricks tables are addressed through Unity Catalog as catalog.schema.table, so both the catalog and schema are required. The token’s identity must be able to create, read, and write tables in that catalog and schema.
See the Azure Databricks connector reference for the full field list.
Create a Project on the Lakehouse
Section titled “Create a Project on the Lakehouse”When you create a new project (Analyze > Projects > New Project), choose your Snowflake or Databricks connection as the project’s data backend. From then on:
- Every table the project’s workflows create, load, or transform is created and written in your lakehouse.
- Reads — in steps, views, and dashboards — run as queries against your warehouse.
- Nothing is copied into the PlaidCloud Lakehouse; the data stays in your account.
Build workflows exactly as you would on the built-in lakehouse — import, transform, join, and publish steps all work the same way. The backend is set when the project is created and applies to the whole project.