Skip to main content

​
What this is

A data warehouse is a centralised, integrated repository that stores large volumes of structured and unstructured data drawn from many systems. It is built to support business intelligence and analytics work by giving that data a scalable, high-performance environment for storage, processing and analysis. For customer data in particular, a warehouse such as Snowflake or BigQuery is often where the most complete view of a customer already lives — joined, cleaned and modelled. Bringing it into Zeotap CDP lets you act on it: make data-driven decisions, personalise customer experiences, sharpen marketing strategies and improve overall customer satisfaction. Data Warehouse is the source category that brings that data in. Instead of exporting files and pushing them to Zeotap, you give Zeotap CDP read access to a table — or, for Amazon S3, to a bucket and folder — and Zeotap pulls records from it on the schedule you choose. It is also the literal label you select in the source-creation form: every connector listed on this page is created by choosing Data Warehouse as the Category. Data Warehouse sources carry both Customer Data (CE) and Non-Customer Entity (NCE) data, so the same category covers customer profiles and the reference data around them — product catalogues, order data, campaign data and similar.

​
Prerequisites

Before you create a Data Warehouse source, confirm:
  • You have permission to create sources in your Zeotap CDP tenant.
  • You have an account with the warehouse or storage service, and the data you want to import already resides in it.
  • You have read credentials Zeotap CDP can use, and the exact location of the object to read — warehouse, database, schema and table for Snowflake; project, dataset and table for BigQuery; bucket, bucket region and folder for Amazon S3; catalog, schema and table for Databricks; database and table for Microsoft Fabric. Each connector’s setup page lists the precise fields it asks for.
  • You know the region of upload for the source, and your Catalogue has the target attributes defined, or you are ready to add them during mapping.

​
Key concepts

Every Data Warehouse source, whichever connector you pick, is configured around the same few settings.
  • Category and Data Source — in CREATE SOURCE you first choose Data Warehouse as the Category, then the specific connector as the Data Source. The rest of the form is connector-specific.
  • Sync Frequency — how often Zeotap CDP re-reads the source. The Microsoft Fabric source form labels this Refresh Frequency. The first sync runs as soon as the source is created; every sync after that follows the frequency you set. Some connectors add further fields for these cadences — for BigQuery and Databricks you also set the sync time and, for a weekly or monthly cadence, the day. The cadences on offer differ by connector, so check the setup page for the one you are creating.
  • Delta Queries Selection — offered by the connectors that support incremental sync. Set to true and Zeotap CDP gathers only the additions and changes made since the last sync; the connector’s setup page names the fields that identify them. Set to false and the full dataset is retrieved and transferred on every sync, whether or not anything changed. For a Snowflake source, prepare the table as described on its setup page before you switch delta queries on.
  • Data Entity — mark the source as Customer Data or Non Customer Data, depending on whether the records describe customers or the entities around them.
  • Region of upload — the storage region for the ingested records, chosen when you create the source. See Region of Storage.

​
Choose a connector

Zeotap CDP offers five Data Warehouse source integrations.

Snowflake

BigQuery

Amazon S3

Databricks

Microsoft Fabric

Pick the connector that matches where the data sits today. There is no need to move data between warehouses first — each connector reads the system it is named for.
Amazon S3 is object storage, not a query engine. Zeotap CDP does not run a query against it — it reads the objects held in the bucket and folder you point it at. Amazon S3 sits in this category because it is configured and scheduled exactly like the warehouse connectors: read credentials, a location, a Sync Frequency and a Delta Queries setting, all under the Data Warehouse category in the source form. If instead your data leaves its origin system as CSV or JSON files delivered to Zeotap over SFTP or Zeotap Google Cloud Storage, use Flat Files.

​
Create a Data Warehouse source

The first four steps are identical for all five connectors. The connection fields in step 5 are the part that differs, and each connector’s setup page walks through them field by field.
1

Open the Sources application

In the Zeotap CDP App, navigate to Integrate → Sources.
2

Start a new source

Click CREATE SOURCE. Expected result: the source-category picker opens.
3

Choose Data Warehouse as the Category

Select Data Warehouse. Expected result: the Data Warehouse connectors are listed as the available Data Sources.
4

Choose the Data Source

Select Snowflake, Google BigQuery, Amazon S3, Databricks or Microsoft Fabric. Expected result: the configuration form for that connector opens.
5

Enter the source details

Give the source a descriptive name, select the region of upload, and choose the Sync Frequency. Then enter the connection details for your warehouse or bucket and set the Data Entity. Where the connector supports incremental sync, also set Delta Queries Selection. For the exact fields and where to find each value in your own account, follow the connector’s setup page linked in the table above.
6

Review and create

Check the values you entered and create the source. Expected result: the new source appears on the Source Listing page.

​
Verify the source was created

The source is configured correctly when:
  • It appears on the Source Listing page under the name you gave it.
  • Its Implementation Details tab shows the connection parameters you entered.
  • The first data sync begins once the source is created, and later syncs follow the Sync Frequency you selected.
The initial transfer scales with the volume of the table or bucket, so a large dataset takes longer than a small one to complete its first sync.

​
FAQ

Because that is how it is created: the source form asks you to choose Data Warehouse as the Category before you select Amazon S3 as the Data Source, and the settings that follow — credentials, location, Sync Frequency, Delta Queries Selection — are the same ones the warehouse connectors use. Amazon S3 itself is object storage: Zeotap CDP reads the objects in the bucket and folder you name rather than querying a table.
As often as the Sync Frequency you set when creating the source. The first sync runs immediately on creation, and every sync after that follows the chosen cadence. For a daily, weekly or monthly cadence you also pick the time of day and the relevant day. The cadences offered differ by connector, so check the setup page for the one you are using.
No, where the connector supports incremental sync. Set Delta Queries Selection to true and Zeotap CDP fetches only the additions and changes made since the last sync. Leave it false and the full dataset is transferred on every sync, regardless of what changed. A Snowflake table needs preparing before delta queries can be used — its setup page covers the steps.
Yes. Data Warehouse sources support both Customer Data (CE) and Non-Customer Entity (NCE) data. Set the Data Entity field on the source to match what the table holds: customer profiles, or reference data such as product catalogues, orders and campaigns.

​
Next steps

Last modified on October 6, 2026