Real-Time Review Intelligence Pipeline
Streaming NLP enrichment platform for customer sentiment
End-to-end streaming data pipeline that ingests customer reviews, enriches them with sentiment analysis and entity extraction in real time, and persists results to an analytical store.
Visual evidence
Streaming NLP & Kafka Ingestion Architecture
Supplementary evidence
End-to-End Streaming Architecture
Case study
Problem, Build, Outcome
Problem
Batch processing of customer feedback creates hours of latency before negative sentiment or emerging product issues can be detected and acted upon.
Build
Built a stream processing architecture with Kafka, Spark Streaming, fine-tuned transformer models, and real-time dashboarding for instant sentiment awareness.
Outcome
Reduced time-to-insight from hours to under 5 seconds, enabling immediate alerting on sudden sentiment drops and automated escalation.
Architecture
System Flow
Architecture Notes
- Kafka message broker decouples high-throughput review ingestion from downstream processing.
- Spark Streaming applies vectorized NLP inference with dynamic batching for maximum throughput.
- Analytical sink stores enriched records with partition pruning for sub-second dashboard queries.
Engineering choices
Technical Decisions
Selected Spark Streaming with micro-batching for throughput efficiency over per-event processing.
Distilled a large transformer model to a smaller architecture to sustain required streaming throughput.
Implemented dead-letter queues to isolate malformed payloads without halting the streaming pipeline.
Evidence
Proof Points
Sub-5-second end-to-end processing latency from ingestion to analytical storage
Fine-tuned lightweight sentiment model optimized for streaming throughput
Automated alert triggering on negative sentiment spikes across product categories
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