REST Connector for Data Analytics Environments
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Solution Overview
Problem
In data analytics environments, the varying interfaces, objects, and operations across different applications and data sources create implementation differences that hinder access to data, making it challenging for customers to consume data from transactional applications for analytics workloads.
Innovation Solution
A REST-based interface and connector system that can be configured via a user interface, providing a connectivity layer that works with multiple REST implementations, allowing access and manipulation of data from various cloud applications and platforms, including SaaS sources, by establishing connections to REST endpoints and supporting multiple authentication mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a data analytics customer seeks to consume data from transactional applications with different interfaces and operations, then data access capability is improved, but system complexity increases due to implementation differences
Solution Approach 1:
The patent introduces a REST connector as an intermediary layer between the data analytics environment and various transactional applications. This connector handles the complexity of different interfaces, objects, and operations by providing a standardized REST-based interface, thereby enabling versatile data access without increasing user-side complexity
Solution Approach 2:
The REST connector provides a universal interface that can connect to multiple types of data sources with different implementations. By standardizing the interaction protocol, the system achieves multi-functionality in data access while maintaining simplicity in the analytics environment
2Adaptability or versatility
If users configure connections to access data from various databases or data sources, then data accessibility is improved, but configuration complexity increases
Solution Approach 1:
The system allows users to configure connections by changing parameters such as endpoint URLs, authentication credentials, and data mapping settings. The REST connector handles the complex parameter transformations internally, enabling users to access diverse data sources with minimal configuration effort
3Quantity of substance
If data is extracted from cloud applications and platforms, then data availability for analytics is improved, but data volume and processing complexity increase
Solution Approach 1:
The REST connector extracts only the necessary data from cloud applications and platforms by implementing selective data retrieval based on user-defined filters and parameters. This extraction approach maintains data availability for analytics while reducing the volume of data that needs to be processed within the analytics environment
Data Source
AI summary
In accordance with an embodiment, described herein is a system and method for providing a REST-based interface and connector for use with a data analytics environment, such as, for example, a business intelligence environment, database, data warehouse, or other type of environment that supports data analytics. A data analytics environment can expose a REST connector that can be configured via a user interface, to provide a connectivity layer that works with a range of REST implementations. A user can create and/or configure one or more connections, to support access to data provided by a variety of databases or data sources. The interface and connector establishes a connection to one or more REST endpoints which can be used to access and manipulate data from a variety of cloud applications and platforms, for example SaaS sources, for analytic workloads.