Cherre Egress for BigQuery allows Cherre customers to access most of their transformed data in their own BigQuery data warehouse, allowing Cherre to be incorporated into larger data strategies and integrate with additional data sets within the organization.

The implementation of the egress is very straightforward and leverages Google’s native data sharing capabilities. The basic steps of the implementation are:

Once these steps are completed, clients will have direct access to their Cherre data in BigQuery.

The Cherre Egress for BigQuery uses Google’s Analytics Hub to deliver a read-only copy of select Cherre data, including custom ingest data and Data Partner specific, into a client hosted BigQuery environment.

The Analytics Hub listing will be initially seeded with data that has already been ingested by Cherre to date, and then updated incrementally based on the client’s Egress configuration. Generally this is daily. Access to the shared data is controlled by client-managed BigQuery IAM roles and permissions.

Before starting the implementation of a Cherre Egress for BigQuery, Cherre needs the contact information for someone that has the required roles to be able to view and subscribe to a listing in Analytics Hub.

Once Cherre has these details, Cherre will create a private listing and share the details with the contact provided.

Once Cherre has successfully deployed and configured Cherre Egress for BigQuery for the client, the client needs to subscribe to the private listing provided by Cherre.

Here is a sample listing:

Once this is complete the Cherre Egress for BigQuery is live!

The volume and frequency of the actual data being egressed is highly dependent on the dataset. For example, data being ingested daily will have daily egress records whereas data being ingested monthly will only produce records monthly. Clients should consider this when applying data quality monitors to their Cherre Egress for BigQuery data. Most data reliability tools can dynamically support this.