Supabase leads in npm + PyPI downloads at 2x; Great Expectations leads in active contributors with 1 (30d); Supabase leads in code adoption with 16.6K repos importing; Supabase leads in job demand with 5 mentions. Updated daily with live metrics.
Data sources: npm, PyPI, GitHub, Crunchbase, Hacker News, Reddit, job boards. Methodology →
Great Expectations: 856.6K npm + PyPI downloads/mo (+6% MoM), 11.3K GitHub stars, 1 active contributors, 284 repos importing, $61M funded vs
Supabase: 2.1M npm + PyPI downloads/mo, 99.5K GitHub stars, 1 active contributors, 3.0K dependents, 16.6K repos importing
Source: AI-Buzz Developer Adoption Index (DAI). Updated daily from 12 data sources. Methodology →
Supabase leads across 6 of 7 key metrics
Downloads (30d)
Disclosed Funding
GitHub Stars
Package Dependents
Repos Importing (code adoption)
HN Mentions (30d)
Momentum (0-100)
For production adoption: Supabase is depended on by 3.4K packages versus 104 for Great Expectations. Supabase leads in package downloads with 2.1M per month compared to 856.6K for Great Expectations (Great Expectations +6% MoM). Supabase appears in 16.6K GitHub repositories compared to 284 for Great Expectations.
For growth trajectory: Supabase has 99.5K GitHub stars versus 11.3K for Great Expectations. Supabase received 3 Hacker News mentions in the last 30 days.
For longevity/risk: Great Expectations has raised $61M in total disclosed funding. Great Expectations's most recent round was Series A. Great Expectations attracted 1 active contributors in the last 30 days compared to 1 for Supabase. Great Expectations operates in AI Infrastructure while Supabase focuses on Developer Tools.
Supabase leads on developer adoption with 2.1M monthly package downloads. Great Expectations has 856.6K monthly downloads and is growing its developer footprint. For a deeper look, visit each company's full profile for trend charts, funding rounds, and community sentiment data.
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| Metric | Updated Mar 23 | Supabase Updated Mar 23 |
|---|---|---|
| Website | greatexpectations.io → | supabase.com → |
| Description | Data quality framework for validating and profiling data pipelines | Open-source Firebase alternative built on Postgres |
| Data Confidence Source: AI-BuzzUpdates: DailyNote: Composite score (0-100) based on field completeness, metric freshness, and identifier coverage. Higher = more reliable data.Methodology → | Excellent (92%) | Excellent (93%) |
| Signal Coverage Source: AI-BuzzUpdates: DailyNote: Number of non-null adoption metrics tracked. Companies with more signals have more reliable composite scores.Methodology → | 4/7 metrics | 7/7 metrics |
| Total Disclosed Funding Source: Public records / manual researchUpdates: WeeklyMethodology → | $61M | - |
| Last Funding | Series A Feb 2022 | - |
| Developer Adoption | ||
| Momentum | 41Moderate | 60Moderate✓ |
| npm Registry Downloads (30d) Source: npm registryUpdates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 0 | 1.7M✓ |
| PyPI Registry Downloads (30d) Source: PyPI (Google BigQuery)Updates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 856.6K✓ | 442.4K |
| PyPI Trend (30d) | ||
| Total Downloads - npm + PyPI (30d) Source: npm + PyPI registriesUpdates: DailyNote: Sum of npm and PyPI; excludes other package managersMethodology → | 856.6K+6% | 2.1M✓ |
| Active Contributors/Day Source: GitHub APIUpdates: DailyNote: Tracks designated public repos per company, not all company GitHub activityMethodology → | 1✓ | 1 |
| npm Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct and dev dependentsMethodology → | - | 3.0K |
| PyPI Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct reverse dependencies from PyPIMethodology → | 104 | 392✓ |
| Code Adoption (repos) Source: ecosyste.ms Packages APIUpdates: DailyNote: Approximate count; excludes forksMethodology →Exclusive | 284 | 16.6K✓ |
| HN Discussion Share Source: Hacker News (Algolia API)Updates: DailyNote: Share of HN mentions within the company's primary categoryMethodology → | - | 60% of HN mentions |
| Status | Private | Private |
| HN Mentions (30d) Source: Hacker News (Algolia API)Updates: DailyMethodology → | 0 | 3✓ |
| HN Mentions Trend (30d) | ||
| Reddit Mentions (30d) Source: Reddit (search API)Updates: DailyNote: Counts posts/comments mentioning the company by nameMethodology →Exclusive | - | 1 |
| Job Mentions (30d) Source: Job board aggregationUpdates: DailyNote: Counts job listings mentioning the company's technologyMethodology →Exclusive | 1 | 5✓ |
| GitHub Stars Source: GitHub APIUpdates: DailyNote: Stars are bookmarks — a popularity signal, not a usage indicatorMethodology → | 11.3K | 99.5K✓ |
| Founded | 2018 | 2020 |
| Primary Category | ||
| Data Last Updated | ||
Data sources: npm, PyPI, GitHub, Crunchbase, Hacker News, Reddit, job boards. Methodology →
Licensed CC BY 4.0. Free to cite with attribution.
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Comparing npm Downloads over 30 days: Supabase (1.7M).
Source: npm registry | Methodology | CC BY 4.0
| Date | Great Expectations | Supabase |
|---|---|---|
| Mar 22 | - | 1.6M |
| Mar 23 | - | 1.7M |
