Configurable Data Analytics Platform Using Reusable Software Modules
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Solution Overview
Problem
Large organizations face inefficiencies and errors in software development due to repetitive tasks in building software and data pipelines, necessitating a configuration-based approach for efficient feature delivery based on reusability of software modules.
Innovation Solution
A method involving a processor that transforms user job requests into directed acyclic graphs (DAGs), constructs software programs using reusable modules, and applies AI algorithms to determine suitable modules for execution, compatible with Spark Structured Query Language for structured data processing, while retraining the AI using historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a configuration-based approach is used to provide a data analytics platform, then feature delivery efficiency is improved and software module reusability is enhanced, but the initial system complexity and setup time increase
Solution Approach 1:
The system is segmented into distinct modular components including a configuration manager, AI algorithm selector, software module library, and execution engine. Each component has a specific function and can be independently developed, maintained, and reused across different analytics projects, thereby improving feature delivery efficiency while managing complexity through structured organization.
Solution Approach 2:
The configuration-based platform is designed as a universal system that can handle multiple types of data analytics tasks through a common framework. The reusable software modules and AI algorithms can be applied across different projects and use cases, enhancing both feature delivery efficiency and module reusability while the modular architecture manages the inherent complexity.
2Loss of time
If reusable software modules are utilized in a library, then development time is reduced and errors are minimized, but the difficulty of managing and organizing the module library increases
Solution Approach 1:
The system incorporates feedback mechanisms where the configuration manager automatically tracks which software modules are used in which projects, their performance metrics, and compatibility information. This feedback is used to continuously optimize the module library organization, improve search and retrieval efficiency, and reduce the difficulty of managing and organizing modules while maximizing reuse benefits.
Solution Approach 2:
An intermediary configuration management layer is introduced between the software module library and the execution engine. This intermediary automatically handles module registration, version control, dependency resolution, and compatibility checking, thereby reducing development time and errors while managing the complexity of the module library through automated mediation.
3Measurement precision
If an AI algorithm is applied to select software modules, then module selection accuracy is improved, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary actions by pre-training AI algorithms on historical software module usage data, project requirements, and performance metrics before actual feature delivery. This pre-computation stores learned patterns and decision rules that can be quickly applied during runtime, thereby improving module selection accuracy while reducing the computational resources and processing time required during actual execution.
Data Source
AI summary
A method and system for providing a data analytics platform that facilitates efficient feature delivery based on reusability of software modules are provided. The method includes receiving a job request that corresponds to a feature desired by a user; transforming the job request into a directed acyclic graph (DAG) that includes a set of operations; and constructing a software program that is configured to execute the set of operations included in the DAG. The transformation is performed by extracting a set of configuration instructions that respectively correspond to operations included in the set of operations from the job request. The construction of the software program is performed by retrieving software modules that are configured to execute operations included in the DAG from a library that stores a plurality of reusable software modules that respectively correspond to algorithm functions.