Strategy and Solutions

We created an agile development process, which facilitated rapid testing and continuous improvement. Our team accelerated the launch of BlueData’s Big-Data-as-a-Service Epic CloudTech product by co-developing their enterprise admin console. As the engagement continued with Bluedata, we built custom data connectors and implemented reporting services with the executive dashboard using iOS (iPad and iPhone). We used Django based python framework for UI services and delivered partial UX design services for the Web UI. Earlier, we chose the Bootstrap.js template. However, we replaced it later with a custom Javascript template for Web UI. We organized the Javascript project using Backbone.js and added numerous JQuery and custom controls. Our team created a Jenkins and Maven-based CI process and used Git for the software repository. We delivered limited QE and testing services that mostly spanned the front-end tier.

The Client

Bluedata is a leading software company headquartered in Santa Clara, California. It provides big data analytics via self-service, through on-premise private clouds. Bluedata’s flagship product is a software solution that gives Hadoop big data deployments, the benefits of virtualization and private clouds, resulting in reduced deployment complexity with improved business agility.

Key Outcomes

Developed HP Enterprise's analytics orchestration admin console, pipelines, and MLOps.

Developed enterprise-grade Admin console with SaaS multi-tenancy and RBAC.

Business Challenges

Bluedata wanted to build a comprehensive, modern, and responsive Enterprise UI for Hadoop-as-a-Service. They were previously following a milestone-based development process and wanted to accelerate the launch of Big-Data-as-a-Service Epic CloudTech products.

Business Impacts / Key Results Achieved

Bluedata achieved an agile development process, which enabled rapid testing and continuous improvement. They built a comprehensive, modern, and responsive Enterprise UI for Hadoop-as-a-Service. The solution involved leveraging common datacenter infrastructure for big data, support for the virtualization of big data infrastructure, provisions for physical clusters of servers, and to orchestrate and meter big data jobs on tenancy boundaries.