Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
0 AWS reviews
-
5 star0
-
4 star0
-
3 star0
-
2 star0
-
1 star0
External reviews
406 reviews
from
and
External reviews are not included in the AWS star rating for the product.
Montecarlo recommendation
What do you like best about the product?
Good Anamoly detection methods,easy to setup,get alerted on slack abd pagerduty on data anamolies.
What do you dislike about the product?
Detail drill down or links that could help to find the Root cause.
What problems is the product solving and how is that benefiting you?
Easy and early detection of data anamloies in the lower environments which will help to mitigate the issues in production
Useful data checks
What do you like best about the product?
- Variety of check types
- Intuitive UI
- Option to build monitors as a code
- Intuitive UI
- Option to build monitors as a code
What do you dislike about the product?
- Notifications cannot be customized
- Custom metrics cannot be scheduled via cron
- Custom metrics cannot be scheduled via cron
What problems is the product solving and how is that benefiting you?
Automation of data quality checks to have a better visibility of data issues if any.
Interesting product but needs a lot improve
What do you like best about the product?
1. The result visualization is useful.
2. The customized query is super helpful when we need to design some complicated alerts.
3. The yaml generation function in UI is also helpful so we can make sure the new code can always be in the correct new format.
2. The customized query is super helpful when we need to design some complicated alerts.
3. The yaml generation function in UI is also helpful so we can make sure the new code can always be in the correct new format.
What do you dislike about the product?
1. ML thresholds do not work well. We are missing lots of important alerts, just because the thresholds go really wide and we are not aware of the issue at all.
2. The MaC keeps on changing, the definition, the structure, the scope, everything keeps on changing frequently, we have to keep on changing our code, which is super annoying.
3. Why do you decide to remove the freshness monitor from MaC and have to let us manually add in a weird other notification place? I do not get the design now. It makes things chaotic.
4. Sometimes dry run passed but after merging the PR, the apply fails. I feel there are some inconsistency there and it is really confusing.
2. The MaC keeps on changing, the definition, the structure, the scope, everything keeps on changing frequently, we have to keep on changing our code, which is super annoying.
3. Why do you decide to remove the freshness monitor from MaC and have to let us manually add in a weird other notification place? I do not get the design now. It makes things chaotic.
4. Sometimes dry run passed but after merging the PR, the apply fails. I feel there are some inconsistency there and it is really confusing.
What problems is the product solving and how is that benefiting you?
We are able to keep on monitoring some important BQ tables.
Montecarlo feedback
What do you like best about the product?
Ease of Use and the table/field lineage is very helpful along with the refresh time, alerts, row change and lot of areas were covered.
Integration is very easy.
Integration is very easy.
What do you dislike about the product?
Sometimes the incorrect data was shown which is little misleading
What problems is the product solving and how is that benefiting you?
row count change, table and field lineage and the custom alerts to notify the change of data.
The definitive way to monitor your data for anomalies
What do you like best about the product?
Monte Carlo makes it really easy to create monitors and alert your team when something triggers your monitors. There is a thorough edit history and ways to test your monitors.
What do you dislike about the product?
The main downside of Monte Carlo is sometimes not having the easiest way to know if you wrote your queries correctly for your monitors. This is mostly a user training issue though, but there are AI tools that help you too.
What problems is the product solving and how is that benefiting you?
Understanding when unexpected behavior in our systems are happening
Monte Carlo Product Review
What do you like best about the product?
UI is great
Ease of setting up alerts
Data observability focused product
Ease of setting up alerts
Data observability focused product
What do you dislike about the product?
Automatic threshold algorithm - it is hard to make it work for all possible timeseries
Limited automation capabilities
Limited automation capabilities
What problems is the product solving and how is that benefiting you?
Data quality for BQ tables
Great product for any organization that values data standards and quality
What do you like best about the product?
I've found field lineage to be far more useful than I originally imagined. The table importance scale is also very nice to see. It has allowed us to get ahead of data quality alerts before our stakeholders are even aware of anything wrong
What do you dislike about the product?
There should be a way to save or create a set of investigation queries for common alerts. May it's the way we implemented MC but the alerts could be moree useful maybe like a git style, what has changed between the last successful query and the one that broke
What problems is the product solving and how is that benefiting you?
It has been instrumental in being ahead of our stakeholders when it comes to changing data or data inconsistencies. Being on the data platform team, it's our responsibility to ensure robust and useable data for everyone, they trust the data that we provide and we must maintain high standards for our team and company so that stakeholders can make high impact choices.
Easy to use with a wide selection of useful defaults
What do you like best about the product?
How easy it is to setup monitors, the wide selection of default monitors available.
I also like how one can comment on alerts and view their history.
I also like how one can comment on alerts and view their history.
What do you dislike about the product?
The limitation on number of values we can segment a query by.
What problems is the product solving and how is that benefiting you?
Monitoring data quality and preventing issues from manifesting into customer environments.
Great product for data teams
What do you like best about the product?
Monte Carlo provides many useful details about an asset, such as the queries that were ran, providing a clear look into the reported anomalies. As a data engineer, it also provides integration with Slack, allowing us to receive alerts there.
Being able to create monitors with code also makes our process easier. I also appreciate the insight reports that can be used to improve our team's data governance.
Being able to create monitors with code also makes our process easier. I also appreciate the insight reports that can be used to improve our team's data governance.
What do you dislike about the product?
I would like to be able to change the landing page of Monte Carlo, instead of the default of Alerts.
What problems is the product solving and how is that benefiting you?
It allows a clear overview of my database tables and highlights when there are issues so they can be resolved quickly. This helps to improve our data reliability and accuracy that ultimately benefits business teams.
A great and valuable observability platform with a great support ecosystem
What do you like best about the product?
Comprehensive Monitoring: The automated monitors track data freshness, volume and schema changes. Additional monitor can track quality across multiple sources with some manual setup.
Fast Issue Detection: Speeds up incident discovery and resolution, helping reduce the time bad data goes undetected.
Scalability: Works well across large, complex data ecosystems with minimal performance impact.
Integration-Friendly: Supports a wide range of data warehouses, lakes, pipelines, and BI tools.
Support: Support team is professional and provides answers in a very timely manner. Product team is very cooperative and open to ideas/improvments
Fast Issue Detection: Speeds up incident discovery and resolution, helping reduce the time bad data goes undetected.
Scalability: Works well across large, complex data ecosystems with minimal performance impact.
Integration-Friendly: Supports a wide range of data warehouses, lakes, pipelines, and BI tools.
Support: Support team is professional and provides answers in a very timely manner. Product team is very cooperative and open to ideas/improvments
What do you dislike about the product?
Cost: Pricing can be high, pricing policy sometimes changes.
Ramp-up Time: While setup is generally straightforward, configuring monitors effectively for all business-critical datasets can still take effort.
False Positives: Especially early on, teams might experience a higher volume of alerts that need tuning to avoid noise and fatigue.
Ramp-up Time: While setup is generally straightforward, configuring monitors effectively for all business-critical datasets can still take effort.
False Positives: Especially early on, teams might experience a higher volume of alerts that need tuning to avoid noise and fatigue.
What problems is the product solving and how is that benefiting you?
Monte Carlo solves the problem of data trust by our data consumers.
The main benefit is that we catch data problems early, before business users notice, which protects trust in our data products and saves significant time troubleshooting.
It also reduces the operational burden on our engineering and analytics teams, allowing them to focus more on delivering value instead of firefighting data issues.
The main benefit is that we catch data problems early, before business users notice, which protects trust in our data products and saves significant time troubleshooting.
It also reduces the operational burden on our engineering and analytics teams, allowing them to focus more on delivering value instead of firefighting data issues.
showing 31 - 40