Brewers Data Provider: From the Establishment Record to the Duty Ledger · Head-to-head
Open Brewery DB - Free Brewery API & Bulk Datasets vs Open Brewery DB - API Documentation
Which brewers data provider: from the establishment record to the duty ledger data fits your job: Open Brewery DB - Free Brewery API & Bulk Datasets, or Open Brewery DB - API Documentation. API, files, or your warehouse. Daily, weekly, or hourly.
Open Brewery DB - Free Brewery API & Bulk Datasets
Open Brewery DB - API Documentation
Coverage, side by side
| Open Brewery DB - Free Brewery API & Bulk Datasets | Open Brewery DB - API Documentation | |
|---|---|---|
| Geographic | 23+ countries, 213+ states/provinces; US 8,224, Germany 1,445, Australia 514 | Mirrors the underlying corpus worldwide |
What each contains
Pick by fit, not by loyalty.
| Open Brewery DB - Free Brewery API & Bulk Datasets | Open Brewery DB - API Documentation | |
|---|---|---|
| Publisher | Open Brewery DB (community-maintained open-source project) | Open Brewery DB (same project, different artifact) |
| What the record is | The corpus itself: every establishment as a complete row | The interface contract: the published specification for querying those rows |
| Records covered | 11,822 establishments - breweries, cideries, brewpubs, bottleshops | Same 11,822-record corpus, described rather than carried |
| Documented fields | 16, definitions marked verified | 16, with per-field type and nullability notes |
| Classification values | 14 observed in the data, led by micro (5,837) and brewpub (3,929) | 10 enumerated, with `large` and `bar` flagged deprecated |
| Geographic coverage | 23+ countries, 213+ states/provinces; US 8,224, Germany 1,445, Australia 514 | Mirrors the underlying corpus worldwide |
| Shapes offered | JSON, CSV and SQL, roughly 180 MB uncompressed | JSON objects of roughly 400 bytes each |
| Datadory rubric | 10/10 - a mark shared by 145 of 1,744 cataloged datasets | 9/10 - a band shared by 534 |
| Best for | Landing the whole corpus at once: CRM loads, market sizing, model features | Building against the live interface: parser conformance, lookups, drift checks |
Or take both in one feed
They stack naturally, because one is the manual and the other is the freight. Build against the documented contract first: parse the sixteen fields, honor the nullability notes, respect the deprecation flags. Land the bulk rows next, pinned to the packaged snapshot. Then leave the aggregate-counts view running as the reconciliation step after every load - when its total leaves 11,822, community edits have moved the corpus and your copy is due.
Two seams deserve a line in the runbook. Classification drift: fourteen values circulate in the data while the printed list carries ten with two deprecated, so map types explicitly rather than trusting any single enumeration. Sparse optionals: address_2 fills near 3%, address_3 is effectively empty, and coordinates, phone and website are nullable - geocode from address_1 plus city and postal code, not from the second line.
The join itself is the easy part: one row per establishment on both sides, keyed by the same obdb-id, so reconciliation is an equality check rather than a fuzzy match.
Or take both in one feed. Datadory normalizes each record to its documented dictionary, attaches sample rows for validation, and ships them beside the rest of the Brewers slice - delivered daily, weekly, or hourly, your call.
API, files, or your warehouse. Daily, weekly, or hourly.
Fair questions
Which record tells you more about each brewery?
Neither adds columns - both dictionaries carry the same sixteen fields. The difference is annotation: the corpus record supplies observed population rates (`address_2` near 3%, `address_3` effectively unpopulated) and the full fourteen-value classification split, while the documentation supplies per-field types, nullability notes and deprecation flags for `large` and `bar`.
How current is the brewery data on each side?
Both describe the present state of a continuously edited database, not an archive - there is no history of past states on either side. The packaged bulk snapshot is versioned 2024.1, while community edits continue to land behind it; the aggregate-counts view (total, by state, by country, by type) is the quickest way to spot when the corpus has moved. Delivery cadence is your call: daily, weekly, or hourly.
Can Datadory deliver both records together?
Yes - sample both and pick by fit, or take both in one feed. Datadory normalizes each to its documented sixteen-field dictionary, attaches sample rows for validation, reconciles on the shared obdb-id, and ships them beside the rest of the eight-record Brewers slice, delivered daily, weekly, or hourly.