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Snowflake, dbt and Salesforce - a match made by Omnata

Posted by James Weakley | February 10, 2021

Omnata was built to make operational analytics easier. Omnata began with the only native mechanism for real-time data access between the core Salesforce CRM platform and Snowflake.

From our real-world experience and speaking with customers, it is clear that real-time access to large external datasets is a serious challenge, but the need tends to evolve. Often, the first challenge for data and operations teams is enriching Salesforce with smaller datasets like lead scores and aggregated values, where periodic updates are OK.

Enter Omnata Push

For those smaller, standard objects and fields that can happily fit on the Salesforce platform, there are many ways to replicate data. Tools are emerging with friendly UIs, however, despite their ease of use, we believe that building business logic in a middle layer is a no go. We’re not alone in this camp, it mirrors the microservices philosophy of "smart endpoints and dumb pipes".

So, we thought about the priorities of data teams and the existing tools that they use and concluded that outbound data integration could be further simplified. We built a data-engineer friendly experience that uses our native Salesforce app and the capabilities of Snowflake external functions. However, we can't ignore the combination of dbt and Snowflake, an increasingly popular combo and for good reason.

dbt packages make it even easier

dbt offers a great framework for managing change. One of the challenges with GUI-driven tools is promoting change through environments. dbt overcomes this by allowing developers to easily test a code-driven, version-controlled pipeline, and allow them to deploy it to production with the comfort that it’ll work the same way.

Omnata Push leverages Snowflake External Functions to bulk-load Salesforce data using SQL queries. Our dbt package extends this by providing job tracking tables and a custom materialization that makes creating load tasks as simple as creating a dbt model.

For data-teams, this means business logic stays in the source of truth and outbound integration is a natural extension of your data pipeline. For operations teams, it means you don’t need to worry about maintaining load jobs or whether ingested data is accurate. And for your sales and service users, they can now leverage analytical outputs and contextual data in their workspace.

See it in action

Get the dbt packages

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