What is Eddytor?

Eddytor is a master-data management platform for editing and governing tabular data, built on Apache Arrow and Delta Lake. It gives teams real tables - with schemas, constraints, version history, and rollback - backed by open formats in your own cloud storage.

The problem it solves

Plenty of important data never lives in a proper system: product master data, mappings between systems, reference lists, the spreadsheets analysts maintain by hand. That data still needs structure, validation, and history - but standing up a database for it is overkill, and a spreadsheet gives you none of the guarantees.

Eddytor is the middle ground: spreadsheet-easy editing on top of real Delta tables, so the data is queryable, versioned, and governed from day one.

How it works, in one paragraph

Eddytor connects directly to your object storage (S3, GCS, or Azure Blob) and reads and writes Delta Lake tables there. It never copies your data into Eddytor - your buckets stay the source of truth, and Apache Arrow is the in-memory format used to move data around. Column-level governance rules ("domains") are stored as metadata on the Delta table itself, so the rules travel with the data.

What you get

• Real tables - typed columns, nullability, and a fully-qualified name (catalog.schema.table).
• Version history & rollback - every write is a Delta version you can inspect, diff, time-travel, or revert.
• Data quality - column domains (allowed values / constraints) validated on write.
• Open & portable - standard Delta Lake on standard object storage; no proprietary lock-in.
• Self-hostable - run the whole stack yourself; you own the data and the deployment.

Who it's for

• Operators standing up and running Eddytor for their organisation - the primary audience of these docs.
• Data & analytics teams who need governed master data feeding their pipelines and reports.
• Anyone who wants versioned, validated tables without operating a database.

Next

• Architecture - the moving parts.
• Quickstart - get it running.