Augmenting Snowflake data sharing with Looker

  • 28 March 2022
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This content, written by Joel McKelvey, was initially posted in Looker Blog on May 30, 2019. The content is subject to limited support.

Businesses today are finding it increasingly valuable to share data beyond their own enterprise data warehouse — both inside and outside their organizations. Some are using data to surface key insights between departments or between companies, while others are using it to help monetize their data. While historically data sharing has involved the copying and/or movement of large data sets, data warehouse vendors like Snowflake have made it easier than ever to share data between organizations or groups of organizations.

While the ability to share data with ease has increased, mechanisms for sharing the accompanying business logic and modes of analysis alongside the data itself are still lacking. This business logic is extremely valuable because it contains a consistent interpretation of the data, designed by your data experts, that simplifies analyses and unifies metrics between data consumers.

In this blog, you’ll get a better understanding of how the Looker project import feature uniquely complements data sharing, allowing for the simultaneous sharing of business logic (and data models) in a way that is governed, scalable, and efficient.

Sharing data in Snowflake

Where data sharing has historically been manual, repetitive, and highly technical, cloud-based data warehouse Snowflake’s sharing is different. Built around a metadata architecture and the decoupling of the storage and compute aspects of a data warehouse, Snowflake makes sharing simple, secure, and nearly instantaneous.

Rather than being copied, data shared with Snowflake is made available in-place. This means it’s always the latest data and never needs updating. Snowflake data sharing also avoids the arduous complexity of data copying and sending copies via FTP that is so familiar to data teams. However, while sharing data is be made simpler in this way, analysis of that data can still remain complex.

Enter the need for shared data and business logic.

Sharing data AND business logic

With Looker, sharing governed business logic can be accomplished in a centralized, reusable, and scalable manner. These capabilities paired alongside modern data warehouses like Snowflake change how the sharing of data and business logic can be done, as noted in by our friends at Hashpath.

One key to sharing business logic alongside data sharing is the use of Looker’s unique project import feature. Because business logic in Looker is codified via LookML, it can be collaborated upon, shared, and controlled. With project import, data sharing is augmented by a simple process for the distribution of data models, metrics, and other key aspects of data analysis. If you’re interested in learning more about the details of project import, check out from Looker’s own Kevin Marr.

Project import + Snowflake

When combined with Snowflake data sharing, project import is a great way to share data and business logic simultaneously. Allowing data teams to build a one-to-one or one-to-many processes facilitates shared model development in a distributed, scalable manner. In addition, project import ensures that everyone who is sharing data is also speaking and leveraging the same language of shared logic based on the expertise of those who developed that logic.

For data consumers, project import alongside data sharing simplifies the modeling and analysis of data. With project import, Looker users can share models and parts of models with other organizations in a highly flexible manner, allowing for sharing that’s customizable while remaining easy to control and track.

Project import also allows for the use of pre-built data models — or Looker Blocks — to simplify the analysis of a wide range of pre-modeled data. Blocks like those from are already built to simplify the analysis of data shared using Snowflake data sharing. Other include models for Salesforce data, Google AdWords, AWS admin data, in addition to blocks for specific analytics use cases such as user behavior analysis by , retail sales forecasting by , marketing agency analytics by , cohorting, forecasting, log analysis, and more. With project import and Looker Blocks, analysts can leverage the work of others to simplify and speed up their own efforts, and data consumers can more easily discover and share insights to fuel their business decisions.

Learn More

Check out our to learn more about project import, the power of , or chat with the to discuss your thoughts and questions about blocks, project import, and the sharing of business logic.

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