Data quality and observability · November 2026 Edition
Great Expectations vs Ataccama ONE
Zero of fourteen models named Great Expectations first on the direct prompt; zero named Ataccama ONE. Great Expectations was named by thirteen of the fourteen models and Ataccama ONE by eleven and Great Expectations carries 36 labels and Ataccama ONE 15, so the shares are not directly comparable.
Named in two categories this edition.
Named in three categories this edition.
First-choice share4%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate22%7%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#8A position in a field of 14; printed, not drawn.
Labels3615A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Great Expectations reading right to left. Rank and label count are printed, not drawn.Monte Carlo was named alongside these two in nine of the fourteen direct answers. Metaplane vs Great Expectations · Metaplane vs Ataccama ONE · Monte Carlo vs Great Expectations
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the data quality and observability page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Great ExpectationsFirst choices, of fourteen modelsAtaccama ONE
Direct00
Paraphrase021 against Ataccama ONE
Comparative01
Budget-constrained20
Scale-constrained00
Negative008 against Great Expectations
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Great Expectations and Ataccama ONE stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | ||||
| GPT-5.4 mini | | | ||||
| Gemini 3.5 Flash | | | | |||
| Perplexity Sonar | | | | |||
| Grok 4.20 | | | | | ||
| Mistral Small | | |||||
| DeepSeek V4 Flash | | | | |||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | | | | |||
| Kimi K2 | | | | |||
| GLM 4.7 FlashX | | | ||||
| MiniMax M2.5 | | | ||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B |
Great Expectations Ataccama ONE first choice named as an alternative argued againstblank: not namedEach cell is one answer, Great Expectations on the left and Ataccama ONE on the right.The direct prompt
The plain question, one answer per model, grouped by where Great Expectations and Ataccama ONE stood in it.
Neither was named
14 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5Metaplane, New Relic alternatives: Monte Carlo, Motadata ObserveOps
GPT-5.4 miniMetaplane alternatives: Monte Carlo
Gemini 3.5 FlashMetaplane alternatives: Bigeye, Elementary Data, Monte Carlo
Perplexity SonarMetaplane alternatives: Elementary, Monte Carlo, Soda
Grok 4.20Metaplane alternatives: Bigeye, Datadog Observability, Soda
Mistral SmallDatadog Data Observability, New Relic alternatives: IBM Instana
DeepSeek V4 FlashMonte Carlo alternatives: Metaplane, Sifflet
Llama 4 Maverickno first choice
Qwen 3.7 FlashMonte Carlo alternatives: Bigeye, Datadog Data Observability, Soda
Kimi K2Metaplane alternatives: Bigeye, Soda
GLM 4.7 FlashXMonte Carlo alternatives: Bigeye, Sifflet
MiniMax M2.5Monte Carlo alternatives: Acceldata, DQLabs, Metaplane, Soda
GPT-6 LunaSoda alternatives: Datadog Data Observability, Monte Carlo
Muse Glimmer 30BBigeye alternatives: Acceldata, DQLabs, Metaplane, Soda
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Great Expectations leads by three points.
Great Expectations3%#4 of 11Ataccama ONE0%#– of 11Mid-marketThe figures above
Level: the same share of first choices.
Great Expectations4%#5 of 14Ataccama ONE4%#8 of 14Enterprise
The order flips: Ataccama ONE leads at enterprise.
Ataccama ONE2%#7 of 15Great Expectations0%#14 of 15What the models said about Great Expectations
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Best avoided if you need real-time monitoring or handle large datasets, due to scalability limitations.” Mistral Small · negative prompt · hard negative
“Open-source data quality engines (like Great Expectations or Soda) ... self-hosting and maintaining them at scale requires significant engineering bandwidth.” Gemini 3.5 Flash · negative prompt · soft negative
“Steep setup for production... documentation gaps for advanced use cases... Some users abandon it for custom solutions” Grok 4.20 · negative prompt · soft negative
“Best ultra-budget option: Great Expectations or Soda Core for basic data quality checks” GPT-5.4 mini · budget prompt · first choice
“Start with GX Core or Soda Core for data quality testing” MiniMax M2.5 · budget prompt · first choice
“Best if the company has strong Python engineering skills and wants an open-source, highly customizable framework; however, the team must manage deployment” Perplexity Sonar · paraphrase prompt · alternative
What the models said about Ataccama ONE
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Consider only when the business needs enterprise-grade master-data management, governance, complex entity matching, or compliance controls” Perplexity Sonar · paraphrase prompt · soft negative
“Data Quality | Informatica, Ataccama | Talend, Oracle, Precisely” GLM 4.7 FlashX · comparative prompt · first choice
“I recommend starting with Ataccama ONE or Talend Data Fabric.” Grok 4.20 · paraphrase prompt · first choice
“🏆 Top Recommendation: Ataccama ONE” Kimi K2 · paraphrase prompt · first choice
Also compared
Metaplane vs Great ExpectationsMetaplane vs Ataccama ONEMonte Carlo vs Great ExpectationsMonte Carlo vs Ataccama ONESoda vs Great ExpectationsSoda vs Ataccama ONESoda Core vs Great ExpectationsSoda Core vs Ataccama ONEGreat Expectations vs BigeyeGreat Expectations vs ElementaryBigeye vs Ataccama ONEElementary vs Ataccama ONEAll twenty-eight head-to-head pages and the full data observability standing
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.