Unify & Scale Marketing Data with
the DiGGrowth-Snowflake Connector

The DiGGrowth Snowflake Connector enables seamless data transfer from marketing platforms like CRM, advertising tools, and web analytics into Snowflake without coding. It automatically gathers, cleanses, and structures the data before securely transferring it into Snowflake. This integration allows marketing teams to quickly access and analyze data, make informed decisions, and scale analytics, all while reducing reliance on IT and minimizing manual data handling.

Yes, the DiGGrowth Snowflake Connector allows you to customize the data fields imported from Snowflake. You can select specific data fields that align with your marketing objectives and exclude unnecessary ones. This flexibility ensures that only the most relevant data is brought into your analytics environment, making it easier to focus on key metrics and drive actionable insights.

To authenticate the Snowflake connection in DiGGrowth, you must provide your Snowflake account credentials, including your account name, user credentials, and the relevant database or warehouse details. DiGGrowth will guide you through a secure authentication process, establishing your connection properly. Once authenticated, your marketing data can seamlessly integrate into Snowflake for analysis and reporting.

To view Snowflake data insights in DiGGrowth, simply connect your Snowflake account to the platform and select the data sets you wish to analyze. DiGGrowth will automatically pull the data into its interface, where you can explore various reports, dashboards, and visualizations. The platform allows you to filter and customize the data to generate meaningful insights, helping you make informed, data-driven decisions directly from your Snowflake data.

While integrating Snowflake with DiGGrowth, you can filter data by selecting specific data fields, applying date ranges based on your marketing objectives. DiGGrowth provides an intuitive interface where you can choose the data sources you want to import and set conditions for filtering out irrelevant data. This ensures that only the most relevant and actionable data is transferred into Snowflake, streamlining your analytics and improving the quality of insights generated.