Real-Time Voting Data Pipeline
Fault-tolerant distributed election data processing architecture
High-throughput, fault-tolerant data pipeline that simulates election voting events, processes them through streaming infrastructure, and displays live aggregate metrics with zero data loss.
Visual evidence
Real-Time Election Operations Dashboard
Supplementary evidence
Distributed Streaming Architecture
Supplementary evidence
Data Pipeline Flow
Case study
Problem, Build, Outcome
Problem
High-volume event processing systems risk dropped events, double-counting, and state corruption during network partitions or node failures.
Build
Constructed an event-driven pipeline using Kafka with exactly-once semantics, stateful streaming aggregations, and idempotent persistence layers.
Outcome
Successfully processed sustained high-throughput vote events with zero message loss and real-time dashboard updates reflecting live totals.
Architecture
System Flow
Architecture Notes
- Event generator simulates realistic multi-region voting patterns with configurable volume.
- Kafka cluster with replication factor guarantees message durability against broker failure.
- Stream processor maintains in-memory state with periodic WAL commits to durable storage.
Engineering choices
Technical Decisions
Used transactional Kafka producers and consumers to enforce end-to-end exactly-once guarantees.
Designed idempotent database write operations so replayed events cannot corrupt running totals.
Separated real-time aggregation from historical audit logging to isolate analytical and compliance workloads.
Evidence
Proof Points
Exactly-once processing semantics preventing duplicate counts or dropped votes
Stateful streaming aggregations with checkpointing and automatic recovery
Live analytical dashboard powered by Streamlit updating in real time
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