Building a data reconciliation system from scratch requires decisions at several layers: how to connect to sources, how to run comparisons, how to orchestrate runs, and how to alert on findings. Each of these layers has free and open-source tooling that is production-grade. Some tools address a single layer; others span multiple.

This list covers the tools worth evaluating at each layer, what they specifically do well for reconciliation use cases, and where they fit in a complete reconciliation architecture.

1. Great Expectations (Data Validation and Quality Gates)

Great Expectations is the most widely adopted open-source data validation framework for Python. You define "expectations" - assertions about what your data should look like - and run validation suites that check whether your data meets them.

For reconciliation, Great Expectations is most useful at the source validation layer: confirm that each source's data is internally consistent before attempting cross-source comparison.…