Big Data Media Intelligence, Entity Recognition & Real-Time Broadcast Telemetry
A big data media intelligence platform processing 8–10 TB of media feeds for automated sentiment analysis, named entity recognition, and real-time newsroom insights.
Operational Constraints & What Was in the Way
Legacy friction, system bottlenecks, and functional limits requiring custom engineering.
Operational & Engineering Context
A major media intelligence firm ingesting global digital publications, video broadcasts, and news feeds was overwhelmed by massive data volume and multi-language entity resolution.
Identified Friction Points
Ingesting high-volume multimedia feeds
exceeded legacy Elasticsearch indexing limits
Identifying corporate brand mentions across 40 languages
required automated NLP entity recognition
PR and media relations customers
required instant real-time sentiment alerts on breaking news events
Execution Pipeline & Architectural Topology
Interactive multi-stage topology diagram demonstrating the system pipeline engineered by Techdome.
End-to-End Production Architecture
Microservices & Business Logic
The computational engine running core workflows for AI & Automation. Orchestrates data transformations, authorization rules, and external integrations.
- Stateless JWT Sessions
- Idempotent Mutation Handlers
- Distributed Event Bus
Delivered Engineering Capabilities
Core production modules and capabilities deployed to live environments.
Distributed Kafka streaming cluster processing continuous unstructured multimedia feeds
ShippedTransformer NLP inference pipeline performing named entity recognition and multilingual sentiment scoring
ShippedHigh-performance ClickHouse analytical datastore executing aggregated brand queries across terabytes in milliseconds
ShippedReal-time customer notification engine
Shippeddelivering instant breaking-news alerts via webhooks and email
Engineered a petabyte-scale distributed ingestion pipeline using Apache Kafka, ClickHouse, and PyTorch transformer models to index, transcribe, and analyze global media streams in real time.
Verified Production Technologies
Categorized technology stack, infrastructure primitives, and execution frameworks running in production.
Presentation & Client Layer
Application Core & Services
State, Search & Persistence
Cloud, Infrastructure & DevOps
Production Telemetry & Milestone Ledger
Verified production status, milestone delivery sign-off, and ground-truth metrics.
Media data processed for sentiment & entity NLP
Automated entity recognition and sentiment intelligence
Confidentiality & Verification Assurance
To protect confidential client intellectual property and enterprise non-disclosure agreements, engagements are presented by industry sector, architectural challenge, and operating geography rather than registered trademarks.
Every architecture, milestone, and timeline listed in this case study is grounded in active production repositories.
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