Secure Dataframe Service for Policy-Governed Data Processing
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Solution Overview
Problem
Existing big data systems require developers to invest significant resources in setting up complex infrastructure and managing compute clusters for data analysis, diverting attention from the actual business logic and increasing organizational costs.
Innovation Solution
A dataframe as a service system that abstracts infrastructure management, allowing users to input hints and business logic, which automatically determines data sources, allocates resources, and executes the logic, providing results in a dataframe format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If developers set up complex infrastructure and manage compute clusters for data analysis, then data processing capability is improved, but device complexity and loss of time increase
Solution Approach 1:
The patent extracts the infrastructure management layer from the user's control. The system provides a dataframe as a service where the underlying compute clusters, resource allocation, and infrastructure are abstracted away. Users only interact with the high-level dataframe interface, while the system automatically handles the complex infrastructure underneath through mechanisms like automatic resource provisioning and cluster management.
Solution Approach 2:
The patent introduces an intermediary layer between the user and the infrastructure. The dataframe service acts as this intermediary, receiving user queries, translating them into appropriate compute tasks, managing resource allocation, and returning results. This mediator handles the complexity of infrastructure management while providing a simple interface to users.
2Power
If developers manage compute clusters and configure resources, then data analysis capability is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring and pre-provisioning compute clusters and resources. When a user requests a dataframe service, the infrastructure is already in place or is automatically provisioned in advance. This eliminates the need for users to spend time on initial infrastructure setup and configuration.
Solution Approach 2:
The dataframe service implements self-service mechanisms where the system automatically manages its own infrastructure. Resource allocation, cluster provisioning, and configuration are handled autonomously by the system based on user requests, eliminating the need for manual infrastructure setup and reducing time loss.
3Power
If organizations deploy extensive applications to configure resources and query data sources, then data processing capability is improved, but device complexity and loss of time increase
Solution Approach 1:
The patent creates a universal dataframe service that handles multiple functions through a single interface. Whether users need data querying, transformation, aggregation, or analysis, they all interact through the same dataframe service. This multi-functional approach eliminates the need for users to deploy and configure separate applications for different data processing tasks.
4Power
If developers configure resources and manage infrastructure, then data analysis capability is improved, but productivity decreases
Solution Approach 1:
The patent extracts the time-consuming infrastructure management tasks from developer work. By providing a high-level dataframe service interface, developers can focus entirely on business logic and analysis rather than spending time configuring resources and managing clusters, thereby improving productivity.
Data Source
AI summary
The present application discloses a method, system, and computer system for providing a dataframe as a service. The method includes (a) receiving, from a client system, one or more hints identifying parameters for source data; (b) executing a plan for a source dataframe including determining whether accessing the source data for the source dataframe is permitted based at least in part on one or more security policies, wherein the plan is based at least in part on the one or more hints; (c) receiving from the client system a business logic pertaining to a transformation to be applied to the source dataframe; and (d) providing to the client system information pertaining to an execution result obtained based at least in part on the business logic.