Multi-Tenant Analytics Platform for Secure Unified ETL Queries
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Solution Overview
Problem
In SaaS domains, integrating data from multiple modules into a common schema for analytics poses compliance and security challenges, and separate analytics solutions lead to maintenance and consistency issues, while on-premise deployment is costly and requires specialized skills.
Innovation Solution
A flexible, modular, and developer-configurable analytics platform that allows module owners to define ETL pipelines, processes data per client requirements, provides a unified interface, and supports high data integrity and scalability, with features like real-time data ingestion, secure operation, and automatic load testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from multiple modules is merged into a common schema for analytics, then analytics capability is improved, but compliance and security issues worsen
Solution Approach 1:
The patent implements a multi-tenant analytics platform where each client has an isolated analytics workspace with separate data schemas, ETL pipelines, and query environments. This segmentation allows analytics capabilities to be provided to multiple clients simultaneously while maintaining data isolation, thus resolving the contradiction between improved analytics capability and compliance/security requirements.
Solution Approach 2:
The patent introduces a platform layer as an intermediary between raw data sources and analytics consumers. This intermediary layer provides standardized ETL pipelines, data transformation capabilities, and query processing while maintaining data isolation through tenant-specific schemas. The intermediary enables analytics functionality without direct exposure of raw data, addressing both analytics capability and security concerns.
2Object-affected harmful factors
If separate analytics solutions are implemented in each module, then data security is improved, but maintenance and consistency issues worsen
Solution Approach 1:
The patent creates a universal analytics platform that serves multiple clients and modules through a common infrastructure. The platform provides standardized ETL pipelines, data transformation logic, and query processing capabilities that can be reused across different tenants. This universality maintains data security through isolation while reducing maintenance complexity through shared codebases and consistent deployment patterns.
Solution Approach 2:
The patent merges separate analytics solutions into a unified multi-tenant platform while preserving data isolation through logical separation. By combining infrastructure, deployment pipelines, and core processing logic into a single system, the patent reduces maintenance overhead and ensures consistency across tenants, while maintaining security through tenant-specific data schemas and isolation mechanisms.
3Object-affected harmful factors
If on-premise analytics deployment is implemented, then data control is improved, but cost and skill requirements worsen
Solution Approach 1:
The patent implements a SaaS-based analytics platform that provides self-service capabilities to clients. The platform includes automated ETL pipeline configuration, built-in data transformation templates, and user-friendly query interfaces that reduce the need for specialized skills. Clients can deploy and manage their analytics workloads without requiring extensive in-house expertise, while the platform handles infrastructure management, scaling, and maintenance automatically.
Data Source
AI summary
The present invention discloses an analytics platform (102). In one embodiment, the analytics platform (102) allows multiple module owners to define their individual Extract, Transform, and Load (ETL) pipelines, processes and stores data as per client requirements, an provides an unified interface at a query level. In one embodiment, an application developer (112) may configure the analytics platform (102) to build an analytics pipeline, and configures an application (104) to work with the analytics platform (102). Further, the application (104) is configured to send data to the analytics platform (102) for ingestion. Further, queries are created upon generation of data transformation logic. Furthermore, widgets associated with the queries are determined to generate reports and dashboards. The generated reports, from the analytics platform (102), are further used by an analyst (110) for business use cases.