data-engineering-and-pipelines

What problem does it solve?

Organizations struggle to move data reliably between systems while maintaining data quality, observability, and recoverability in the face of schema drift, late-arriving data, and failures.

Core Features & Use Cases

• End-to-end data pipeline design for ETL/ELT, batch, streaming, CDC, and event-driven flows with explicit handling for replay, backfill, and schema evolution.
• Quality, lineage, and recoverability controls across ingestion, validation, transformation, and publication stages.
• Use Case Example: Build a warehouse loading pipeline that ingests source data, validates schema, deduplicates, handles late data, and provides replayable runs with run IDs and check-pointing.

Quick Start

Create a simple end-to-end pipeline that ingests data, validates schema, and writes idempotently to the sink with run-tracking.