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Compare Great Expectations and Monte Carlo 
At a Glance
Market Segments
Mid-Market (45.5% of reviews)
Enterprise (50.7% of reviews)
Entry-Level Pricing
No pricing available
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Great Expectations
Star Rating
(11)4.5 out of 5
Market Segments
Mid-Market (45.5% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about Great ExpectationsMonte Carlo
Star Rating
(470)4.4 out of 5
Market Segments
Enterprise (50.7% of reviews)
Pros & Cons
Entry-Level Pricing
Contact Us
Browse all 3 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Monte Carlo excels in providing real-time alerts for data quality issues, which has significantly improved users' awareness and responsiveness to ongoing data problems. This proactive approach allows teams to address issues before they impact stakeholders, enhancing overall data reliability.
- Users say that Great Expectations stands out for its user-friendly interface, making it easier for data professionals to define and validate data quality expectations. This simplicity helps users focus more on data analysis rather than getting bogged down by data quality concerns.
- Reviewers mention that Monte Carlo's continuous implementation of new features contributes to its intuitiveness and ease of use, making it a specialized tool for data monitoring and observability. This focus on user experience is reflected in its high satisfaction ratings.
- According to verified reviews, Great Expectations is particularly beneficial for software testers, as it helps bridge gaps between teams and manage large codebases effectively. Users appreciate its ability to validate and customize data expectations, which enhances collaboration and efficiency.
- G2 reviewers highlight that while Monte Carlo has a strong presence in the enterprise market, it has received a higher volume of recent reviews, indicating a more active user base and up-to-date feedback. This suggests that users are consistently finding value in its offerings.
- Users report that although Great Expectations has a slightly higher star rating, its limited number of reviews may not fully capture the breadth of user experiences. In contrast, Monte Carlo's extensive feedback provides a more comprehensive view of its capabilities and user satisfaction.
Pricing
Entry-Level Pricing
Great Expectations
No pricing availableMonte Carlo
Start
Contact Us
All you need to make data reliable for one team
- Monitoring for Data Warehouse, Business Intelligence, ETL
- Incident triaging, troubleshooting and root cause analysis
- Lineage
Free Trial
Great Expectations
No trial information availableMonte Carlo
No trial information availableRatings
Meets Requirements
9.2
11
8.3
437
Ease of Use
9.1
9
8.2
444
Ease of Setup
9.2
6
8.2
308
Ease of Admin
8.3
6
8.5
159
Quality of Support
8.5
10
9.0
391
Has the product been a good partner in doing business?
8.6
6
9.3
162
Product Direction (% positive)
10.0
10
8.9
433
Features by Category
Functionality
Not enough data
9.0
260
Not enough data
8.8
261
Not enough data
7.8
238
Not enough data
8.3
247
Not enough data
7.7
242
Not enough data
7.4
244
Agentic AI - Database Monitoring
Not enough data
7.1
13
Not enough data
6.9
13
Not enough data
6.9
13
Not enough data
7.1
13
Not enough data
6.8
12
Not enough data
6.5
13
Not enough data
7.1
13
Data Management
Not enough data
8.5
51
Not enough data
8.4
47
Not enough data
8.6
51
Not enough data
7.9
49
Agentic AI - DataOps Platforms
Not enough data
7.2
6
Not enough data
6.0
5
Not enough data
6.3
5
Not enough data
6.3
5
Not enough data
6.3
5
Analytics
Not enough data
7.8
50
Not enough data
7.7
47
Monitoring and Management
Not enough data
9.2
55
Not enough data
7.6
48
Cloud Deployment
Not enough data
7.4
43
Not enough data
7.0
41
Generative AI
Not enough data
6.2
34
Not enough data
6.1
34
Functionality
9.4
8
7.4
291
8.8
11
8.8
323
8.8
11
8.1
294
8.9
9
8.0
300
Management
8.3
10
8.7
319
8.9
9
7.7
287
8.5
8
8.3
311
8.7
9
8.0
305
8.6
11
8.1
312
Generative AI
Not enough data
5.8
231
Agentic AI - Data Observability
Not enough data
6.1
27
Not enough data
6.2
27
Not enough data
6.7
27
Not enough data
6.4
26
Not enough data
6.7
29
Functionality
Not enough data
8.1
187
Not enough data
6.4
174
Not enough data
6.7
169
Not enough data
6.1
164
Not enough data
6.4
165
Management
Not enough data
7.2
169
Not enough data
7.5
169
Not enough data
7.9
167
Not enough data
7.4
175
Not enough data
7.5
169
Generative AI
Not enough data
5.2
145
Not enough data
5.3
145
Categories
Categories
Shared Categories
Great Expectations and Monte Carlo are categorized as Data Quality and Data Observability
Unique Categories
Great Expectations has no unique categories
Monte Carlo is categorized as DataOps Platforms and Database Monitoring
Reviews
Reviewers' Company Size
Great Expectations
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Monte Carlo
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Accounting
36.4%
Information Technology and Services
18.2%
Telecommunications
9.1%
Security and Investigations
9.1%
Online Media
9.1%
Other
18.2%
Financial Services
14.2%
Information Technology and Services
11.1%
Computer Software
10.7%
Marketing and Advertising
3.7%
Manufacturing
3.5%
Other
56.8%
Discussions
Great Expectations has no discussions with answers
What is Monte Carlo software?
1 Comment
MV
Monte Carlo is a fully automated, end-to-end data observability platform that helps data engineering teams reduce time to detection and resolution for data...Read more
Monte Carlo has no more discussions with answers
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