Destinations

CSV

CSV exports download your data as a comma-separated values file. No credentials are needed. Wusool generates the file when the export runs; download it once the run succeeds. A CSV export never mixes Simple and Combined dimensions in one file.

Best for: Ad hoc exports, sharing data with stakeholders who do not have Wusool access, seeding other tools manually.

Format:

• Encoding: UTF-8, comma-delimited, first row is column headers
• Simple dimensions: dimension_name, dimension_type, row_index, value, source_dimension_id, source_value_id
• Combined dimensions (one row per combination × source part): combined_dimension_id, combined_dimension_name, dimension_type, row_index, part_index, source_dimension_id, source_dimension_name, source_value_id, source_value

BigQuery

Wusool writes your data directly into a BigQuery dataset you connect via Google OAuth (see Data Connections). One connection is supported per organization, and the dataset must already exist in the BigQuery console.

How it works: each run fully replaces the target table (WRITE_TRUNCATE) with schema autodetect; every row carries an _exported_at timestamp, and empty tables are skipped rather than written. Layout is either one shared table per data type, or one table per dimension/workbook — your choice per export.

Table schemas (shared layout):

• dimensions (Simple values): dimension, value_id, value, sort_order, created_at, modified_at
• combined_dimensions (levelled pair format — an N-source combination emits N-1 rows sharing a combination_id): dimension_name, level, dimension_a/value_a/value_a_id, dimension_b/value_b/value_b_id, row_index, combination_id, created_at, modified_at
• workbooks (tall — one row per cell): workbook_name, workbook_id, row_id, row_index, column_name, column_type, value, value_number, created_at, modified_at. Restore the sheet shape in your BI tool with a PIVOT keyed on column_name.

Per-item layout keeps the same row shapes but splits them into one table per dimension/workbook (workbook tables stay wide instead of tall).

Snowflake

Wusool will write your data directly to a Snowflake table using a service user.

What you will need:

• Snowflake account identifier
• Warehouse, database, and schema names
• A service user with write access to the target schema

How it works: Wusool creates or replaces a table in your schema on each export run. The table name matches the dimension or workbook name.

Amazon S3

Wusool uploads your data as CSV or JSON files to an S3 bucket using an IAM user with scoped permissions.

What you will need:

• AWS region
• S3 bucket name
• Optional: key prefix (folder path within the bucket)
• IAM access key and secret with s3:PutObject permission on the target bucket

How it works: Each export creates or overwrites a file at {prefix}/{connection-name}/{dimension-name}.csv. Each run overwrites the previous file rather than appending to it.

Google Cloud Storage

Wusool uploads your data as CSV files to a GCS bucket using a service account.

What you will need:

• Google Cloud project ID
• GCS bucket name
• Optional: object prefix (folder path within the bucket)
• A service account JSON key with Storage Object Creator role on the target bucket

How it works: Each export creates or overwrites an object at {prefix}/{connection-name}/{dimension-name}.csv.

Azure Blob Storage

Wusool uploads your data as CSV files to an Azure Blob Storage container using a connection string or SAS token.

What you will need:

• Storage account name
• Container name
• Connection string or SAS token with write access to the container

How it works: Each export creates or overwrites a blob at {connection-name}/{dimension-name}.csv in your container.

SFTP

Wusool uploads your data as CSV files to any SFTP server using password or SSH key authentication.

What you will need:

• Hostname and port
• Username
• Password or SSH private key

How it works: Each export uploads files to the remote directory you specify. Files from previous runs are overwritten.