Great Expectations
Great Expectations is an open-source Python library that helps data engineering teams validate, document, and profile their data pipelines.
About this data
Updated June 29, 2026
Overall Pulse Score
+39 over this period
A 0-100 index summarizing the tone of 112 relevant public mentions gathered from public online communities across 21 weeks in the selected period. It measures online sentiment, not a rating of the product's quality.
Weekly Sentiment Trend
Pulse Score by week over the selected period. Each point is one complete week of mentions.
This week in public discussion
Recent discussion around Great Expectations across the past several weeks leaned modestly positive, with commenters most often praising its feature set and integration capabilities, particularly for data quality validation workflows and schema-as-code use cases. Several mentions highlighted reliability as a strength, though a handful of posts flagged bugs and performance concerns, including one report of an exception thrown in version 1.18.1. The pulse score held relatively steady, suggesting a community that is engaged but watching closely for stability improvements.
Read the deeper analysisAI-generated summary of public online discussion during this period. It reflects the tone of that discussion, not facts about the product or our views.
Sentiment mix by week
How the tone of public discussion splits each week.
Most-discussed praise
Most-discussed complaints
Themes across the selected period, with mention counts.
How Great Expectations compares
Pulse Score over the selected period versus the top tracked competitors in Software.
Where the mentions come from
Share of the 112 relevant public mentions in the selected period, by source.
Sample public mentions
Showing 5 of 112 analyzed public mentions in this period, with links to the original source. We do not reproduce full threads.
“PySpark DataFrame validation fails with AttributeError: 'DataFrame' object has no attribute 'reset_index'. ### Description When validating PySpark DataFrames with result formats that extract sample data (SUMMARY, COMPLETE), validation fails with: Environment - **GX Version:** 1.1...”
“UnexpectedRowsExpectation unexpected_rows limit. **Is your feature request related to a problem? Please describe.** Currently, when using UnexpectedRowsExpectation or retrieving unexpected rows in Great Expectations (e.g., result_format: COMPLETE with Spark/SQL), the system retur...”
“US-12: CSV-Validierung mit Great Expectations implementieren. Als Entwickler möchte ich CSV-Dateien mit Great Expectations gegen den Data Contract validieren, damit Schema-, Typ- und Qualitätsfehler automatisch erkannt werden. Priority: P2 Size: XL Definition of Done * Die Bronze...”
“US-11: CSV- und XML-Validierungsstrategie konzipieren. Als Entwicklerteam möchten wir eine Validierungsstrategie für CSV- und XML-Daten entwickeln, damit beide Datenformate zuverlässig geprüft werden können. Priority: P1 Size: M Definition of Done * Great Expectations wurde erfol...”
“MLOps : asset-check Evidently de drift des embeddings entre deux snapshots. ## Contexte Le **monitoring de drift** est absent : rien ne mesure la dérive des embeddings entre deux snapshots OpenAlex successifs. L'asset researcher_embeddings (dataops/citation-dagster/src/citation_d...”
364+ more analyzed mentions, full history, and theme breakdowns are part of Pro.
Get ProDeeper analysis
- Feature praise and integration fit dominated positive discussion, with commenters frequently placing the tool inside multi-component data platforms.
- Sentiment dipped slightly from the prior period and showed an unsteady mid-range trajectory across the four weeks rather than a clear recovery or decline.
- Reliability drew opinion in both directions, with some commenters vouching for it while others cited specific bugs in recent versions.
- A quiet comparative undertone appeared in the discussion, suggesting at least some commenters were weighing alternatives rather than treating adoption as settled.
| Praise theme | Mentions |
|---|---|
| Strong features | 22 |
| Good integrations | 16 |
| Reliability | 9 |
| Easy to use | 4 |
| Feature requests | 3 |
| Complaint theme | Mentions |
|---|---|
| Bugs | 24 |
| Reliability | 12 |
| Missing features | 9 |
| Feature requests | 5 |
| Lacking integrations | 4 |
Discussion of Great Expectations over the past four weeks settled into a moderately positive but uneven tone, with a pulse sitting just above the midpoint and a slight downward drift from the prior period. The dominant thread running through commentary was feature praise, which drew the most mentions by a clear margin, followed closely by positive notes on integration. Several mentions framed the tool as a natural fit inside broader data platform stacks, appearing alongside references to orchestration tools, MLOps portals, and schema-as-code workflows. This context suggested commenters were evaluating Great Expectations less as a standalone utility and more as a component in layered pipelines, which colored the praise with a practical, architectural tone rather than enthusiasm about any single capability.
Sentiment trajectory showed a rocky start to the window, with a low reading in late April giving way to a recovery through early May. Scores then plateaued in the high forties before a noticeable lift in mid-May, only to drift back into the low-to-mid fifties through June. The most recent data points showed a mild oscillation rather than a clean trend, suggesting the conversation was neither gaining nor losing momentum in a decisive direction.
Reliability surfaced on both sides of the ledger, appearing among praise themes and complaint themes simultaneously. This split signaled genuine division in how commenters experienced the tool under real conditions. Bug reports, while modest in count, included at least one specific exception tied to a named expectation rule in a recent version, and this kind of concrete frustration appeared to anchor the skeptical voices. Performance and missing features drew smaller but present complaint clusters.
The sample mentions also hinted at a subtext around alternatives, with at least one discussion framing a competing tool alongside Great Expectations in the context of a data quality decision. That comparative undertone, even if minor, added a layer of evaluative tension to what was otherwise a mostly constructive conversation.
AI-generated summary of public online discussion during this period. It reflects the tone of that discussion, not facts about the product or our views.
Member perspectives
Individual opinions from Pro members, posted over time. These are personal member views, not aggregated sentiment data.
Overall Pulse Score
+39 over this period
A 0-100 index summarizing the tone of 112 relevant public mentions gathered from public online communities across 21 weeks in the selected period. It measures online sentiment, not a rating of the product's quality.
Data summary
Compare with another tool
Great Expectations
56
Trainual
88
Score-level preview from live weekly tracking.
Are you Great Expectations?
Get a private enterprise dashboard for your product - full history, every source, theme deep-dives, and weekly alerts. You can also respond to the data shown here.
Explore the enterprise dashboardAffiliate disclosure
Some links on this site may be affiliate links. If you click one and make a purchase, we may earn a commission at no extra cost to you. Learn more.
Is Great Expectations your product?
See everything behind this page - full history, every source, theme deep-dives, and weekly alerts - in a private enterprise dashboard.
Request early accessCompare with similar tools
Mail Memories
Mail Memories is an email-based service that delivers users their past emails or memories on a scheduled basis.
Paid plans available
View DetailsLookAway
LookAway is a Mac app that reminds users to take regular breaks from their screen to reduce eye strain.
Free tier; paid plans available
View Details