Are you looking to power a customer-facing application, API, or internal tool that supports thousands of users and requires sub-second query latencies using your Snowflake data?
Check out this Propel and Snowflake demo where you will learn how to expose your Snowflake data via a low-latency GraphQL API to power internal and external applications. We will cover how to connect Propel to Snowflake, how to create a Data Pool from your Snowflake table, how to model your data into metrics, and how to query your Metric data via a GraphQL API.
What are the use cases for exposing Snowflake data via a GraphQL API?
- Customer-facing analytics - SaaS and consumer products expose analytics for customers to understand how the product is performing for them via customer dashboards, insights, and reports.
- Data APIs - APIs allow other developers to access the data in a standardized way, without needing to know the underlying SQL structure of the database.
- Data Sharing - Sharing data with partners, vendors, or customers requires a secure and scalable way to expose data.
- Internal tools - Modern organizations require internal tools that have the necessary data built into their workflow.
Why do you need low-latency GraphQL API on top of Snowflake?
Data applications have unique requirements that are different from internal BI. These requirements include:
- ⚡️ Sub-second queries → Customers expect fast, snappy product experiences.While internal employees may be okay with waiting 45 seconds or even minutes for a query to run, this latency is a no-go for customer-facing products where the data analysis is part of the core workflow that the product enables.
- 🌊 High concurrency → Products need to support thousands or millions of users.Because data products ultimately serve customers rather than employees, they must support a dramatically higher number of concurrent requests than what internal data tools are designed to handle. And they must do so seamlessly and cost-effectively.
- 📜 Developer APIs → A contract in code between the apps and the data stack.Shipping a modern data application requires collaboration between data and the product development teams (frontend & backend). APIs codify the contract and SLAs that allow the separation of concerns that keep the product development teams focused on solving customer problems.
- 🔐 App-centric security layer → Each end-user can only see their own data.In SaaS and consumer products, end-users access their own data from the web and mobile applications. This requires an app-centric security layer for multi-tenant or consumer environments, not just the employee-centric role-based access control.
Conclusion
In conclusion, by leveraging low-latency GraphQL APIs on top of Snowflake, you can easily power customer-facing applications with sub-second query latencies, empower developers across the organization to build with data using self-service APIs, and accelerate projects that traditionally take months to deliver in a matter of weeks or even days.
