Development of an Automated Big Data Processing Platform for Scalable Data-Driven Decision Making

A growing financial services organization faces difficulties in delivering large datasets (up to 1 GB text files) efficiently to clients due to resource constraints and manual processing efforts, hindering timely insights and scalability. The client seeks to transition from manual workflows to an automated platform capable of handling diverse data sources and increasing dataset sizes while ensuring high performance and minimal intervention.

A mid-sized financial services firm seeking to automate large-scale data retrieval, transformation, and delivery to enhance client insights and operational efficiency.

• Enable automatic retrieval, validation, pattern analysis, transformation, and delivery of large datasets with minimal manual intervention.
• Process datasets of size up to 1 GB efficiently, reducing processing time and resource usage.
• Implement scalable workflow orchestration supporting future growth, including migration to more advanced systems.
• Facilitate seamless communication and data exchange between multiple development teams and subsystems.
• Deliver an operational Minimum Viable Product (MVP) within 6 to 12 months to accelerate business value realization.
• Improve data processing reliability even in nonstandard scenarios through adaptive extraction mechanisms.

• Adaptive extraction engine capable of handling nonstandard data collection scenarios, including delayed data arrivals.
• Centralized API to enable cross-team communication, metadata management, and integrated workflows.
• Workflow gatekeeper to prioritize and queue data processing tasks, optimizing resource allocation and throughput.
• Unified UI providing a cohesive user experience for monitoring, managing, and troubleshooting data pipelines.

• Data source connectors for retrieving raw datasets from multiple client systems
• APIs for internal communication between subsystems
• Monitoring and alerting tools for system health and performance
• Security and authentication modules for data privacy compliance

• Ability to process datasets up to 1 GB within 8 business hours or less
• High availability with minimal downtime to support continuous data flow
• Scalable architecture supporting future volume increases without significant rework
• Robust error handling, especially for nonstandard data scenarios or delayed data arrivals
• Secure data transmission and storage in compliance with relevant data privacy standards

The implementation of this automated big data processing platform aims to significantly improve data handling efficiency, enabling rapid delivery of large datasets, reducing manual effort, and supporting business growth. The project is expected to facilitate processing up to 1 GB datasets within 8 hours, leading to increased client satisfaction, attracting more enterprise clients, and supporting the organization’s goal of transforming into a data-driven enterprise capable of handling large-scale data needs with minimal intervention.