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Compare Great Expectations and Monte Carlo 
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At a Glance
Market Segments
Mid-Market (45.5% of reviews)
Enterprise (51.2% 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
(462)4.4 out of 5
Market Segments
Enterprise (51.2% of reviews)
Pros & Cons
Entry-Level Pricing
Contact Us
Browse all 3 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Monte Carlo excels in data observability with a score of 9.3, highlighting its robust monitoring capabilities that allow for real-time insights into data pipelines. In contrast, Great Expectations, while still strong, has a slightly lower score of 8.3 in anomaly identification, indicating it may not be as effective in real-time monitoring.
- Reviewers mention that Great Expectations shines in real-time analytics, scoring 9.4, which is significantly higher than Monte Carlo's score of 7.4. This suggests that users find Great Expectations more effective for immediate data analysis needs.
- G2 users highlight the quality of support for Monte Carlo, which received a high score of 9.3. Users appreciate the responsiveness and helpfulness of the support team, while Great Expectations scored 8.5, indicating a solid but slightly less favorable support experience.
- Users on G2 report that Monte Carlo's ease of setup is rated at 8.4, which is on par with Great Expectations' score of 9.2. However, reviewers mention that Great Expectations provides a more streamlined onboarding process, making it easier for new users to get started.
- Reviewers mention that Monte Carlo's data integration capabilities are rated at 8.7, which is competitive, but Great Expectations edges ahead with a score of 8.6. Users appreciate the seamless integration options available in both products, but some find Great Expectations slightly more user-friendly in this area.
- Users say that while both products offer automation features, Great Expectations scores higher at 8.8 compared to Monte Carlo's 8.2. Reviewers appreciate the automated workflows in Great Expectations, which they find particularly beneficial for reducing manual tasks in data quality management.
Pricing
Entry-Level Pricing
Great Expectations
No pricing availableMonte Carlo
Start
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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
430
Ease of Use
9.1
9
8.2
437
Ease of Setup
9.2
6
8.2
303
Ease of Admin
8.3
6
8.5
160
Quality of Support
8.5
10
9.0
386
Has the product been a good partner in doing business?
8.6
6
9.3
163
Product Direction (% positive)
10.0
10
8.9
426
Features by Category
Functionality
Not enough data
9.0
257
Not enough data
8.8
258
Not enough data
7.8
235
Not enough data
8.3
244
Not enough data
7.7
239
Not enough data
7.4
241
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
49
Not enough data
8.5
45
Not enough data
8.6
49
Not enough data
7.9
47
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
48
Not enough data
7.7
46
Monitoring and Management
Not enough data
9.2
53
Not enough data
7.6
46
Cloud Deployment
Not enough data
7.4
42
Not enough data
7.0
40
Generative AI
Not enough data
6.2
33
Not enough data
6.1
33
Functionality
9.4
8
7.3
287
8.8
11
8.8
318
8.8
11
8.1
291
8.9
9
8.0
295
Management
8.3
10
8.7
313
8.9
9
7.8
284
8.5
8
8.3
307
8.7
9
8.0
301
8.6
11
8.1
307
Generative AI
Not enough data
5.8
227
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
184
Not enough data
6.4
171
Not enough data
6.6
166
Not enough data
6.0
161
Not enough data
6.4
162
Management
Not enough data
7.2
167
Not enough data
7.5
167
Not enough data
7.9
165
Not enough data
7.4
172
Not enough data
7.5
167
Generative AI
Not enough data
5.2
142
Not enough data
5.3
142
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
10.9%
Computer Software
10.6%
Marketing and Advertising
3.8%
Manufacturing
3.5%
Other
57.0%
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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