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Cube Dev > Case Studies > Cloud Academy's Accelerated Data Modeling and Reduced Analytics Downtime with Cube
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Cloud Academy's Accelerated Data Modeling and Reduced Analytics Downtime with Cube

Technology Category
  • Analytics & Modeling - Big Data Analytics
  • Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
  • Education
  • National Security & Defense
Applicable Functions
  • Maintenance
  • Warehouse & Inventory Management
Use Cases
  • Picking, Sorting & Positioning
  • Tamper Detection
Services
  • Cloud Planning, Design & Implementation Services
  • Training
The Challenge

Cloud Academy, a San Francisco-based SaaS startup, was in search of a modern analytics platform to enhance its existing enterprise analytics offering. The company needed a solution that could deliver a seamless, highly available embedded analytics experience based on CI/CD best practices, while also allowing for planned outages for necessary infrastructure maintenance. They also needed to leverage their existing data warehouse and maintain high flexibility in their data modeling to serve both internal and external end users. Prior to Cube, Cloud Academy used a major BI platform for their enterprise embedded analytics UI, and internal stakeholders used the BI platform directly. However, they needed a solution that would allow for greater flexibility in data modeling, security context orchestration, and caching.

About The Customer

Cloud Academy is a SaaS startup based in San Francisco, California. The company's e-learning platform is designed to provide tech workers with a personalized upskilling experience in technical subjects such as cloud technology, DevOps, and software development. The platform's training resources and assessments are customized to the user's trajectory, role, and skills. The analytics and personalization granularity of the platform offers organizations robust insights into their teams’ proficiency, helping them to boost employee retention and accelerate company operations.

The Solution

Cloud Academy found Cube in mid-2021 and chose it for its highly flexible caching layer and fully managed, Git-integrated, and zero-downtime deployments. It took ten engineers about a month to onboard and integrate Cube with Cloud Academy’s existing CI/CD platform. After these initial processes, they delivered their first Cube-based analytics app to enterprise customers in less than three weeks. Cube’s flexible data modeling framework allowed the team to use their existing data warehouse as a single source of truth, serving consistent data models to both enterprise customers and internal stakeholders. Cube Store’s fully managed pre-aggregation capabilities boosted the response times of complex data models that serve both internal and external use cases. Cube Cloud’s isolated deployment and Git integration enabled the team to implement true and efficient SDLC, while its RBAC framework served as a component in security context orchestration.

Operational Impact

  • With Cube, Cloud Academy was able to significantly improve their data modeling and analytics capabilities. The flexible data modeling framework allowed them to use their existing data warehouse as a single source of truth, ensuring consistent data models for both enterprise customers and internal stakeholders. The fully managed pre-aggregation capabilities of Cube Store boosted the response times of complex data models, enhancing the user experience for both internal and external use cases. The isolated deployment and Git integration of Cube Cloud enabled the team to implement an efficient SDLC, while its RBAC framework served as a component in security context orchestration. Additionally, Cube’s Alerts feature provided the team with a simple yet effective mechanism to monitor deployments’ API health and pre-aggregation build failures.

Quantitative Benefit

  • Speed up releases of new data models in production by 5x

  • Decreased their platform’s analytics downtime by 90%

  • Delivered their first Cube-based analytics app to enterprise customers in less than three weeks

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