When running any business, especially software as a service (SaaS) applications, companies generate a lot of data. In the modern data stack, developers typically clean, test, and transform this data with tools such as dbt, Airbyte, or Fivetran, and then they store this data in cloud data warehouses like Snowflake.
Data warehouses centralize data from different data silos, including internal databases or SaaS applications, with the ultimate goal being data visualization and analysis. Analytics engineers can also use data warehouses for creating event-driven product features, like automated reporting emails and product notifications.
The data stored in data warehouses like Snowflake is frequently used for internal purposes like business intelligence, but Snowflake is not often used for building customer-facing applications, namely in-product analytics or customer insights.
Querying the warehouse directly from customer-facing applications is slow; costs can get out of control; and building a GraphQL API requires substantial backend infrastructure to serve this data rapidly and securely to the frontend.
In other words, because of the complexity involved in producing a high-performance GraphQL API from a data warehouse like Snowflake, customer-facing applications often can’t leverage the organization’s data warehouse at all — until now.
Today, we are thrilled to announce Propel’s Snowflake Data Source. Teams can now use Snowflake data in customer-facing products without costly data engineering.
Propel offers low-latency response times, easy-to-use GraphQL API, and cost-effectiveness at scale. These features allow developers to build complex products in minutes, including analytics products that serve millions of users.
Propel manages all the caching, optimization, authorization, and APIs so that teams can focus on the product experience, without the overhead of managing highly-available data infrastructure. Our powerful Snowflake integration reduces the level of effort needed to produce scalable experiences when building in-product analytics.
With Propel, organizations are able to build the analytics they’ve always wanted with the teams they already have. Developers just need to connect Snowflake data platform to Propel, and Propel provides a high-performance GraphQL API.
There are many use cases where developers can leverage their Snowflake data using Propel, including:
Many organizations use Snowflake as a data warehouse in order to support business intelligence, event-driven architecture, and other data science projects. However, it’s always been tricky to use data warehouses for anything other than internal manual reporting.
With Propel, developers can easily build in-product analytics for web or mobile apps directly from the data warehouse without data engineering. Our mission is to empower customers with amazing data experiences that always load quickly, whether the application needs to query 30 days or 5 years of data at once.
Additionally, Propel offers powerful filters to help teams easily uncover analytical insights. These filters are fast to build and iterate for the product development teams, improving the developer experience compared to hiring an entire data engineering team.
Of course, analytical data can be useful for more than just visualizations and reporting. In addition to in-product analytics, organizations are able to use Propel to easily automate reporting emails, pulling the necessary analytical data via the GraphQL API and then sending the emails with their existing email stack.
Finally, the ability to use Snowflake as a Data Source means that organizations can provide end customers with direct API access to build their own reports and product experiences. Behind the scenes, Propel handles security, performance, and reliability, while providing a cost-effective and user-friendly interface for software engineers.
We are onboarding Snowflake users first as fast as we can. We can’t wait to see what you can build with Propel!
If you're ready for more, check out our feature demo on how to build Snowflake data apps with GraphQL using Propel.
Product and data teams struggle to work together because there's a tradeoff in data between flexibility, performance and cost-effectiveness.