Application Software Data: Cross-Store Metrics, Open-Source Infrastructure and Vendor Directories · Head-to-head
GitHub REST API (Repositories & Search) vs FRED - Federal Reserve Economic Data (St. Louis Fed)
Which application software data: cross-store metrics, open-source infrastructure and vendor directories data fits your job: GitHub REST API, or FRED - Federal Reserve Economic Data. API, files, or your warehouse. Daily, weekly, or hourly.
GitHub REST API (Repositories & Search)
FRED - Federal Reserve Economic Data (St. Louis Fed)
What each contains
Pick by fit, not by loyalty.
| GitHub REST API | FRED - Federal Reserve Economic Data | |
|---|---|---|
| Record identifier | `id` numeric repository identifier (670418243) with `node_id` GraphQL node string; `full_name` joining owner and repo | `series_id` short code selecting one series, e.g. MMMFFAQ027S |
| Human-readable label | `full_name` plus the maintainer-written `description` | `title` ('Money Market Funds; Total Financial Assets, Level') plus optional `notes` |
| Change timestamp | Three per object - `created_at`, `updated_at` and `pushed_at` | `last_updated` revision stamp per series; `observation_start` and `observation_end` bounding the history |
| Measured quantity | Counters on the object: `stargazers_count`, `forks_count`, `watchers_count`, `open_issues_count`, `network_count` | One `value` per dated `date` on each observation; missing points written as '.' |
| Classification vocabulary | — | `units`, `frequency` and `seasonal_adjustment`, each carried with a short-code companion field |
| Demand or popularity signal | Star and watcher counts as raw community traction | `popularity` integer scoring the series across FRED users |
| Selector vocabulary | — | Request-side `units` transforms and `frequency` plus `aggregation_method` collapsing daily input to annual averages, sums or end-of-period |
Where they're equivalent
More than the artifact-versus-measurement split implies.
- Both score 10 out of 10 - the rubric ceiling shared by 145 of the 1,744 cataloged datasets.
- Both dictionaries are fully verified, every definition checked rather than inferred, matching the 85.7 percent verification rate catalog-wide.
- Both publish one row per thing: one object per repository on one side, one observation per series per period on the other.
- Both come from the platform that produces the data - GitHub's own interface to its own corpus; the Federal Reserve Bank of St. Louis' own aggregation of the series it tracks.
- Both carry a demand signal: star and watcher counts on one side, a user-popularity score on the other.
- Neither substitutes for the other: no repository object holds a dated economic series, and no observation holds artifact metadata. Any workflow wanting both buys both capabilities.
Fair questions
Do the two field dictionaries overlap?
Only in skeleton. Each reduces its world to an identifier, a label, a change timestamp, a measured quantity and a classification vocabulary. Beyond that they diverge: GitHub documents 20 attributes on a repository object, FRED documents 14 fields across series metadata and dated observations.
Which archive reaches further back?
FRED, decisively. Its observation windows default to July 4, 1776, and documented examples walk annual GNP from 1929 onward. GitHub's timeline starts at each repository's creation timestamp, with event-level history since 2011 available separately through the GH Archive.
Which record fits machine-learning feature work?
Both, in different roles. Repository metadata builds entity-level features - traction, language mix, maintenance state - while FRED supplies exogenous macro controls, with ALFRED vintages preventing look-ahead leakage in backtests. Feature-engineering recipes continue on the data scientists guide.
Can I get both records from Datadory?
Yes - sample both, pick by fit, or take both in one feed. Each arrives normalized to its documented field dictionary with sample rows attached for validation, delivered on one schedule - daily, weekly, or hourly - your call - next to the rest of the application software catalog.