Data ETL Tool

The tool enables teams to design end-to-end ETL pipelines by defining sources, transformations, and destinations in a structured, portable configuration. It centers on reproducibility, testability, and exportability of data workflows. Users can capture multiple data sources (databases, files, APIs), apply mappings, type casting, cleansing, and enrichment rules, and route results to warehouses, lakes, or streaming sinks. A DAG-based execution plan provides a conceptual view of dependencies, while built-in validation checks guard against schema drift and incompatible data types. The system generates an exportable configuration (JSON/YAML) that can be version-controlled and integrated into CI/CD pipelines. Unique value lies in its ability to simulate runs with sample data, verify row counts, and produce lineage information without moving real data. Core features include source and destination definitions, transformation rule sets, schema validation, configuration export, and test runs. Optional capabilities include incremental loading, parallelism controls, data quality checks, and catalog integration for governance. Use cases span cloud migrations, data consolidation from APIs, and orchestration of multi-step data movements across environments. This tool benefits data engineers, data analysts, and operations teams seeking repeatable, auditable, and portable ETL configurations that align with deployment pipelines.