A fast-growing developer platform with over half a million apps built
Retool enables businesses to quickly build software by connecting to an existing database or API. Companies like Zappos and Doordash use Retool to create new web applications, mobile apps, and even AI / ML logic workflows.
The data team’s focus at Retool is straightforward: create a scalable data system that enables Retool and its customers to get value from their data both today and in the future. To do so, they hire data-savvy team members and provide them with the necessary, governed data for stakeholders—from marketing to HR to product—to self-serve their data needs.
Empowering both internal business users and Retool customers with data activation
Business users leverage the shared data to create reports and insights. Retool’s data is also used beyond reporting to automate processes, improve efficiency, and increase productivity.
The same data activation approach is also reflected for Retool customers. For example, customer Zappos rebuilt their entire enterprise demand planning system using Retool. In less than 3 months they connected their myriad of data sources, addressed years of backlogged feature requests, saved thousands of dollars by consolidating tools, and made the relevant data analytics team 20% more efficient.
Providing holistic views to different teams
The Retool data team also creates and maintains key dashboards. One of them, a Customer Success (CS) “mega dashboard,” ties together data from multiple sources—including Salesforce and machine learning models—to calculate customer health. In this central dashboard, CS can leverage the insights to drive customer satisfaction, improve retention, and identify expansion opportunities.
A simple, stable data structure with dbt Cloud and Databricks
Retool is a fast-growing and fast-moving company with new feature launches, expanding sales motions, increasing data volume, and growing teams. This means the data team has to stay nimble so they can adapt to the company’s evolving data needs. With this in mind, Retool prioritized ease of set-up, minimal maintenance, and scalability for their data infrastructure.
Setting up dbt Cloud to empower a small team
Retool started using dbt several years ago when the company was much smaller and employees wore many hats. The tool brought an efficient data workflow with built-in collaboration and continuous deployment. It also decreased maintenance and troubleshooting time with out-of-the-box automated testing and data quality alerting features.
“dbt Cloud allowed Retool to get value out of data really quickly,” shared Samuel Garfield, Analytics Engineer at Retool. “We were generating large quantities of user data on product usage and needed insights without hiring a dedicated data team first. Engineering, growth, and CS could build out and maintain data models on their own.”
Integrating Databricks Data Intelligence Platform
Databricks' Data Intelligence Platform also offered the same ease of use, flexibility, and scalability Retool was after. Their lakehouse architecture brought AI and BI development under a single roof and came with “killer” features like Databricks SQL, serverless data warehouse, and Unity Catalog—a unified governance solution. It’s easy to use Databricks SQL to control compute costs and ensure a given dbt job uses the appropriate type and amount of resources. Databricks SQL powers most of the analytics use cases at Retool. The team used the dbt Databricks adaptor to set up and integrate their data.
“The dbt-Databricks adaptor allowed us to automatically integrate our Databricks Unity Catalog to dbt so we didn’t need any extra configuration,” explained Samuel. “All the security and permissions are automatically derived from dbt models, so it is easy to maintain a compliant, end-to-end view of all data and AI assets. It also automatically does Photon optimization, which has been a big contributor to our performance and cost improvements.”
SQL or Python, whenever wherever
Both dbt and Databricks support SQL and Python within the same project. This flexibility enables Retool’s data analysts, business users, and engineers to use whichever language is most accessible to them. This joint workflow incentivizes cross-team collaboration while enabling unified governance.
Moving forward with AI
Retool has migrated existing AI models to Databricks and plans to explore Databricks’ AI & ML features in the future.
“I have to shout out the Databricks Assistant which has made data much more accessible at Retool. Many of our stakeholders are answering their questions by asking the Assistant about our data model. In many cases, the assistant pulls its answers from the dbt documentation itself. This seamless combination of Databricks and dbt Cloud will make it easy to deliver value from the data in ways that our business and stakeholders don’t even imagine,” concluded Samuel.
