Transparent Data Transformation for Governed Cloud Workloads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In cloud environments, High-Performance Computing and machine learning workloads face challenges in granting/gaining data access, particularly in Kubernetes environments where administrators must manually configure access controls for local and remote data.
Innovation Solution
A computer-implemented method that installs data transformation plugins in a dataset, applies dataset-specific policies, and executes data transformations such as anonymization and caching to generate a transformed dataset, providing transparent data access while adhering to data governance policies, with automated node selection and workload deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If administrators manually configure access controls for data in Kubernetes environments, then data security and governance are ensured, but system complexity and administrative overhead increase significantly
Solution Approach 1:
The system implements self-service through automated policy application where the data transformation framework automatically configures and enforces access controls based on predefined policies, eliminating the need for administrators to manually configure each data access scenario while maintaining security and governance requirements
Solution Approach 2:
The framework performs preliminary action by pre-configuring transformation plugins and access control policies before data access requests are made. The system establishes governance rules and transformation logic in advance, so that when data access is needed, the controls are already in place and automatically applied without requiring real-time administrative intervention
2Reliability
If data transformations are applied to datasets for governance compliance, then data security and policy adherence are improved, but processing time and computational resources increase
Solution Approach 1:
The system applies transformations in advance by pre-processing datasets and caching transformed versions before they are needed for workloads. The data transformation framework performs preliminary data anonymization, formatting, and governance-compliant transformations so that when workloads execute, they can use the pre-transformed data without incurring additional processing delays
Solution Approach 2:
The framework creates copies of datasets that are pre-transformed according to governance policies. Instead of transforming data on-demand for each workload, the system generates and stores multiple copies of the same dataset with different transformation applications, allowing workloads to access pre-prepared data versions without triggering additional transformation processing time
3Ease of operation
If transparent data access is provided to end-users, then user workflow continuity is maintained, but data governance control and security management become more difficult
Solution Approach 1:
The data transformation framework acts as an intermediary layer between end-users and the underlying data infrastructure. This intermediary automatically applies governance policies, access controls, and transformations without users needing to understand or configure them. The framework mediates between user requests for data access and governance requirements, translating user needs into policy-compliant data retrieval while maintaining workflow transparency for users and centralized control for administrators
Data Source
AI summary
A computer-implemented method of providing data transformation includes installing one or more data transformation plugins in a dataset made accessible for processing an end user's workload. A dataset-specific policy for the accessible dataset is ingested. A data transformation of the accessible dataset is executed by invoking one or more of the data transformation plugins to the accessible dataset based on the dataset-specific policy to generate a transformed dataset. The user's workload is deployed to provide data access for processing using the transformed dataset in accordance with a data governance policy.