The data pipeline. We spend so much time trying to get data from source to destination, how we actually derive value from that data sometimes feels like an afterthought.

Once raw data is in a data warehouse, users need to model it into a form that’s easy to analyze. For example, a B2B SaaS company that uses Stripe to charge customers can easily get a lot of data about those charges from Stripe’s API into their warehouse (using Fivetran, for example). But that data is not in a format that can easily answer questions:

We can answer these questions with only the raw data. An analyst must first transform the data. Increasingly, analysts are turning to dbt for this job.

Without a doubt, dbt has become a rocketship in the in-database transformation space, helping data teams ship reliable data models faster than ever. In fact, some of their popular Slack community channels now exceed 18,000 members.

dbt, formerly Fishtown Analytics, is laser focused on building their product to be the best choice for SQL-based in-database data modeling. To-date, the solution for enterprises seeking to move that finely modeled customer data from their data warehouse to other apps has been to ask data engineers to build APIs to connect the two. As anyone who has built or managed these APIs will tell you, they break - often.

At SeekWell, we are also very big believers in the power of SQL, which we enable you to use to move your customer data from your data warehouse to the business apps you use every day without tying up your data engineer’s valuable time. You may have heard of this described as reverse ETL or operational analytics.

With SeekWell, you can use SQL to push the data you have modeled with dbt in your database to popular business apps such as Salesforce, Google Sheets, and Slack.

Our customers have quickly realized the power in the joint SeekWell + dbt solution for operational analytics and data modeling. With dbt, they are using the SQL they already know and love to build data models that end users trust directly in their databases. And with SeekWell, they can move that data that already has a stamp of approval from the database to business apps such as Google Sheets, Salesforce, Zendesk, Intercom, HubSpot, Slack, and even other databases for business teams to consume so they can run their regular operations.

Turns out there is another very powerful approach to combining SeekWell and dbt. GoCanvas saves time using SeekWell to prototype data models before building production models in dbt.

GoCanvas uses SeekWell to prototype data models then build mock dashboards in Excel or Google Sheets. With the dashboards, stakeholders are able to provide feedback on the model, which GoCanvas’ data team then edits to the stakeholders’ satisfaction before pushing permanent changes to production with dbt.

SeekWell enables you to easily prototype data models and share them with stakeholders before putting those models into production with dbt.

For GoCanvas, efficient prototyping of data models is an important part of their process. Otherwise, data teams must build new dashboards for feedback from stakeholders, which is time consuming and repetitive. SeekWell helps GoCanvas save time and remove unnecessary builds from the process.

If you are using dbt for in-database SQL-based data modeling, SeekWell can easily help you make sure you are building the models your stakeholders will trust before you push anything to production. That way, both data teams and stakeholders have more time to focus on the parts of their job that add value to your business.

Want to get started with SeekWell for reverse ETL or data model prototyping? Try it for yourself for free today.