A Data Democratization Platform for Data Science Teams
Sammed Kumar
Technical Product Manager
Meda Ajay Krishna
Associate Product Architect
Samuel Jaideep Bethala
Senior Product Architect
Nitish Kumar
Associate Product Architect
Over the last decade, there is a shift in focus towards high value Data Science projects and rapid technology evolution in the Data Science field making it difficult for the enterprises to adapt. This makes Data Scientists turn towards variety of open source alternatives instead of enterprise level proprietary tools. This trend comes up with its own set of challenges like
- Overhead effort in installing, setting up, maintaining and connecting with rest of the enterprise Data.
- Fragmented Data Science projects lead to lack of consistency, trust, duplication of effort and difficulty in collaboration.
- Leadership’s lack of ability to understand, estimate and monitor the Analytics costs across people and technology
The developed solution evaluates tools used by various departments and Data Science teams and provides a unified governance platform to access the tools under an approval-based ecosystem. This provides governed compute environments to ensure standardization; while on the other hand granting the Data Scientist flexibility to modify their own environment as necessary.
This platform also provides –
- Multiple different computes including Databricks, R-Studio, R Shiny deployed on Kubernetes
- Template based development environments with predefined libraries and variety of compute options
- Kubernetes based deployment helps pay per use while providing provision for on-demand scaling
- Ability to setup workflow-based approval process for various assets like Datasets and compute environments
- Access control for the compute and Datasets based on functional area boundaries
- Function and Project based setup for team collaboration.
- Cost Vs Usage reports to help resource administration and forecasting
Request a demo to find out how D Cube can help you to enable frictionless governance for the analytics lifecycle by allowing Data citizens utilize the footprint to its potential while enforcing controlled provisioning of Data and Infrastructure.
