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Cloud-Based Scalable Corpus Platform Development

digiteum.com

Education

Information technology

Media

Legacy System Limitations

Existing corpus-building system (NMC) suffered from scalability constraints, processing limitations (26 million documents over 5 years), and outdated architecture unable to leverage modern cloud capabilities for linguistic data processing at scale.

About the Client

World-leading educational and academic publishing organization specializing in Human Language Technology tools and services

Platform Modernization Goals

Develop cloud-native corpus platform with elastic scalability
Achieve 10x productivity improvement over legacy system
Implement automated linguistic annotation pipeline
Enable modular expansion for new data sources and NLP features
Ensure 24/7 system availability with distributed cloud architecture

Core System Capabilities

Multi-source data ingestion (RSS feeds, news APIs, social media)
Distributed data processing pipeline with deduplication
Linguistic annotation using Stanford CoreNLP/OpenNLP
Dynamic metadata enrichment and content balancing
Modular microservices architecture for feature expansion

Technology Stack Requirements

Microsoft Azure cloud platform

Azure Cosmos DB

Serverless computing (Azure Functions)

Docker containerization

Python-based NLP processing

External System Integrations

Event Registry API
Sketch Engine export interface
Twitter API
Bing Search API
Stanford CoreNLP toolkit

Operational Requirements

Horizontal scalability to 8M+ documents/day
3-hour processing SLA for daily news corpus
99.95% system availability
Multi-terabyte storage elasticity
Role-based access control with GDPR compliance

Expected Business Outcomes

Platform will enable 5x faster corpus growth compared to legacy systems, supporting 1215TB/year data processing capacity. Modular architecture reduces time-to-market for new linguistic features by 60% while cloud economics improve cost efficiency for large-scale NLP operations.