Data Warehouse Schema
This document gives you an overview of the different table schemas used for the various Data Warehouse Solutions.
Descriptions and links for Schema v1, Schema v2, and Schema v3 are included below.
Schema v1 (legacy) | Schema v2 (legacy) | Schema v3 |
Schema v1 is used for the BigQuery. To see details of the table and column descriptions, read more here. Only customers who enabled BigQuery legacy before May 2025 will have access. It's recommended to migrate to the latest Schema v3 to ensure you get access to the latest features. | Schema v2 is used for the AWS S3 Legacy export and is typically used for AWS Redshift and Snowflake solutions. You can read more about the table and column descriptions here. Only customers who enabled AWS S3 legacy before May 2025 will have access. It's recommended to migrate to the latest Schema v3 to ensure you get access to the latest features. | Schema v3 is the latest and recommended version of the Data Warehouse schema. You can connect directly with Google BigQuery. It is available via AWS S3 export used for AWS Redshift, Snowflake, and Databricks solutions. Here, you can read the schema documentation for v3. |
Schema Changes
As Dreamdata's solution develops, so does the schema. Dreamdata is, however, committed to changing the data warehouse schemas in the following way to ensure minimum impact for customers.
- New fields might be added to the existing schemas without notice. Most data warehouses handle this without additional work. Depending on how you query or import the data, you might consider what happens if a new field is added.
- Removing fields. Dreamdata will never remove a field documented in the schema.
- Renaming fields. Dreamdata will never rename a field documented in the schema.
- Change a field type. Dreamdata will never change the field type documented in the schema.
If any schema versions have an end of life date, it will be clearly communicated to customers. Currently there are no end of life for any the schema versions.
