Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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Experience using Monte Carlo
What do you like best about the product?
The ability to automate validation tasks and get alerts has been nice
What do you dislike about the product?
It would be better if there was more information about the model that they are taking into account and how the thresholds are being calculated.
What problems is the product solving and how is that benefiting you?
It has helped us automate certain basic level checks and has been helpful in alerting unexpected trends
Streamlined but not as user-friendly during onboarding
What do you like best about the product?
I like that it's helping us streamline the validation process for our model and it gives us more structure
What do you dislike about the product?
Too many emails flooding my inbox when a rule has been breached; Can't assign users to specific monitoring tasks
What problems is the product solving and how is that benefiting you?
It's helping us streamline the validation process for our model. We're currently updating our model to a new version and the validation process is being set up on Monte Carlo. It definitely adds a lot more structure to our process which we appreciate but I feel like it could be refined more to make the process even smoother. It's not incredibly difficult to onboard a new member of the team to the model but I feel some new features can definitely aid in the process.
Monte Carlo Review
What do you like best about the product?
I like that Monte Carlo is intuitive and easy to use, especially for people who come from a non-technical background. I also like that the Monte Carlo team is responsive and there is to help us.
What do you dislike about the product?
My biggest frustration with Monte Carlo is that there is no coding wrapper that can be used. It is only out of the box or SQL at this point, so more in depth checks are hard to implement.
What problems is the product solving and how is that benefiting you?
Monte Carlo is helping unify the model validation steps. We are able to create checks, schedule them, and receive alerts all in one spot instead of those being 3 different locations. That makes it easier to know where to go . It is also allowing all teams across our organization use and understand the same tool across models. Historically, model validation looked very different across teams - this helps unification.
Reliable Data Observability Platform with Outstanding Support
What do you like best about the product?
What I like best about Monte Carlo is its ability to proactively detect data issues before they impact our business, combined with an easy-to-use interface that makes monitoring data quality straightforward and efficient. The support team is also very responsive and helpful, which makes implementation and troubleshooting smooth.
What do you dislike about the product?
While Monte Carlo offers powerful features, sometimes the initial setup can be a bit complex and may require more detailed documentation or guided onboarding for new users. Also, adding more customizable alert options would enhance its flexibility.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us proactively detect data pipeline failures, data anomalies, and quality issues before they impact our business operations. This reduces downtime, improves trust in data, and allows our teams to focus on insights rather than firefighting data problems.
Monte Carlo is a good tool and save us a lot of time to automatically detect data anomalies.
What do you like best about the product?
I like the automated detection of data anomalies the most.
What do you dislike about the product?
Sometimes, the alerts do not come at real time and they can come a few hours/days later than the actual outage.
What problems is the product solving and how is that benefiting you?
Monte Carlo brought more data observability and quality issues on our datasets.
user friendly and ease of use
What do you like best about the product?
Automated alerts, AI based quality control, User interface is easy to understand, has all the features and functionality that are needed for data quality checks.
What do you dislike about the product?
Sample use cases within the tool to help user understand what all monitors they can create.
What problems is the product solving and how is that benefiting you?
data quality checks to ensure data integrity and accuracy is maintained.
Great observability tool for data engineers, analysts, scientists, and product owners!
What do you like best about the product?
Low-code, no-code solution
The MC platform is very user friendly. Getting familiar to the app is not too difficult, and a lot of the built-in functionality is great!
The MC platform is very user friendly. Getting familiar to the app is not too difficult, and a lot of the built-in functionality is great!
What do you dislike about the product?
One thing I hope that Monte Carlo can implement is the feature for "starring" your favorite monitors/alerts/dashboards. This would make it much easier to get what you are exactly looking for rather than filtering down to your assets.
What problems is the product solving and how is that benefiting you?
Monte Carlo is helping detect anomalies within our data through the different monitors and alerts. I really like how a lot of the core ones for data quality are built in (Freshness, Volume), and the ability to create custom monitors helps a lot. Monte Carlo is also helping analyze which queries we execute are more expensive and compute-intensive. This helps us find ways to optimize our own performance.
Monte Carlo
What do you like best about the product?
Helpful to keep track of data inconsistencies
What do you dislike about the product?
the interface could be more user friendly
What problems is the product solving and how is that benefiting you?
Data discrepancies in our back end systems
Great Experience!
What do you like best about the product?
Monte Carlo makes it easy to catch and resolve data issues before they impact stakeholders. The automated data quality monitoring, lineage visibility, and alerting help us identify root causes quickly. The integration process was smooth, and the UI is intuitive enough that both technical and non-technical users can navigate it with ease. Their customer support team is responsive and genuinely helpful, which makes onboarding and ongoing use even better.
What do you dislike about the product?
Sometimes the initial alert volume can be high until fine-tuned, which may feel overwhelming for new users. While integrations are generally strong, a few niche connectors still require manual workarounds. Pricing can also feel steep for smaller teams, though the value is there once implemented.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us detect and fix data quality issues in real time, so bad data doesn’t make it to reports or dashboards. It automatically monitors pipelines, identifies schema changes or anomalies, and shows clear lineage to trace the root cause. This saves hours of manual investigation, improves trust in our data, and reduces the risk of decision-making based on inaccurate information.
Great for Data Engineers, not that much for other roles
What do you like best about the product?
I like the fact that everything is well integrated with other systems, such as Snowflake or PowerBI
What do you dislike about the product?
It is not very user friendly in comparison to some competitors
What problems is the product solving and how is that benefiting you?
It helps a lot with landing issues from Engineering teams
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