Can you explain how you implemented Backpipeline in your application and what its use case is?

Backpipeline is a custom data ingestion and processing framework we built to handle real‑time analytics for our marketing dashboards. The architecture is modular: a scheduler triggers jobs, a Celery worker pool processes tasks, and a PostgreSQL database stores metadata and results. We use Redis as the broker for Celery, which allows us to scale workers horizontally. The pipeline reads raw data from external APIs, normalizes it, validates schema, applies business rules, and writes cleaned data to a data warehouse. It also supports backfilling for historical data and has robust error handling with retry logic and alerting. The main use case is to provide up‑to‑date insights for marketing teams, enabling them to adjust campaigns in near real‑time. By decoupling ingestion, transformation, and storage, we can add new data sources or change transformations without impacting the rest of the system. Monitoring is handled via Prometheus and Grafana, giving visibility into pipeline health and performance.