iot-data-arch

You design scalable IoT data pipelines with Pathway by applying a polyglot persistence architecture that separates hot, warm, and cold storage layers. This approach supports both real-time streaming analytics and long-term historical data processing within a single ETL workflow.

Handling late data and event-time windowing in IoT ETL pipelines requires defining specific tolerance strategies within your streaming framework. Pathway supports pure-function transformations that natively manage event-time windowing and late data arrivals without coupling to IO operations.

Configuring hot, warm, and cold storage layers for IoT data architecture involves assigning ScyllaDB to the hot layer, Doris to the warm layer, and RustFS to the cold layer. This polyglot persistence strategy optimizes both event-time windowing computations and long-term data retention.

Yes, you can use Pathway to read IoT data streams from Kafka, apply pure-function transformations, and simultaneously write the output to multiple storage systems. This modular ETL design ensures testability while avoiding IO-bound coupling in your pipeline.

Pathway supports pure-function transformations to ensure modular and test-friendly IoT data processing. By avoiding IO-bound coupling, this approach maintains clean architectural boundaries within your streaming ETL pipelines for polyglot persistence environments.