Nexus
Industrial IoT Data Pipeline
High-throughput data pipeline connecting 50+ industrial data sources to a unified analytics layer. Event-driven architecture with sub-100ms data freshness at 500K events per second.
50+
Data Sources
500K/s
Peak Throughput
<100ms
Data Freshness
Context
A warehouse operator had accumulated 50+ disparate data sources — WMS, TMS, conveyor PLCs, RFID readers, barcode scanners, and ERP — with no unified data layer for analytics or ML workloads.
The Challenge
Sources used incompatible protocols (OPC-UA, Modbus, REST, JDBC), had different update frequencies, and produced raw binary formats. Analysts were writing one-off scripts per source; the data team was drowning in integration work.
The Solution
Designed Nexus as a connector-first data pipeline platform. Each source type has a configurable connector that normalizes to a common Avro schema before writing to Kafka. Spark Structured Streaming handles enrichment, deduplication, and routing. The output layer writes to InfluxDB (time-series) and Snowflake (analytics).
Methodology
- Connector framework with 12 pre-built adapters for industrial protocols
- Schema registry (Confluent) for forward-compatible data contracts
- Exactly-once semantics via Kafka transactions and idempotent consumers
- Infrastructure-as-code with Terraform for reproducible deployments
Impact
Reduced integration engineering from 2 weeks per source to 2 hours. Analytics team onboarding time for new data sources dropped from months to days.