Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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Monte Carlo Review
What do you like best about the product?
The flexibility and getting timely and reliable alerts for Volume, Schema and Freshness is useful. Able to tune the model is great.
What do you dislike about the product?
Not dislike, but couple of things that can be better:
1) Dashboards can be better in providing more actionable insights like most frequently failing tables or top 5 failing tables, under which schema, failing for what reason, frequently failing monitors, etc
2) It would be great if any updates made on alerts in Monte Carlo can flow into ServiceNow incidents
3) Additional integrations with files would be great, like if a file has not arrived, etc.
4) If we can have the model tuned for alerts much sooner than 2 weeks would be a welcome move.
1) Dashboards can be better in providing more actionable insights like most frequently failing tables or top 5 failing tables, under which schema, failing for what reason, frequently failing monitors, etc
2) It would be great if any updates made on alerts in Monte Carlo can flow into ServiceNow incidents
3) Additional integrations with files would be great, like if a file has not arrived, etc.
4) If we can have the model tuned for alerts much sooner than 2 weeks would be a welcome move.
What problems is the product solving and how is that benefiting you?
Issues with data quality
Monte Carlo is best-in-class, especially for analytics engineers
What do you like best about the product?
The troubleshooting agent (in preview) has been a particular stand-out. Other than that, I'd say that the customization of alerts appearing in places like Slack is my favorite.
What do you dislike about the product?
The UI is very clunky and the pricing models aren't the most cost-effective.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps with monitoring that our other tools don't address - particularly testing and monitoring our source data that comes from a variety of sources. It allows us to stand up testing for those in a unified and consistent manner. This allows our teams to focus on other technical work.
Makes it very easy to detect issues and stale data
What do you like best about the product?
The machine learning model. It is a good with finding anomalies
What do you dislike about the product?
How too sensitive it sometimes can be. It goes off sometimes too often
What problems is the product solving and how is that benefiting you?
Stale data issue
Anomalies
freshness
Anomalies
freshness
Monte Carlo has been a good tool for data quality and observability
What do you like best about the product?
It helps us catch data issues proactively
What do you dislike about the product?
No problems so far in the use of this software
What problems is the product solving and how is that benefiting you?
It helps us catch data quality issues proactively, which leads to trusts from our user base.
A valuable tool for catching data and performance changes proactively
What do you like best about the product?
I like the customization options of the platform best. We have been able to leverage it as a business performance monitoring tool, in addition to data completeness. The possible use cases are numerous.
What do you dislike about the product?
The largest downside of using Monte Carlo is the learning curve. It took a decent amount of prompting to get our business team comfortable with setting up alerts and using within our daily flow. The amount of options, while helpful, yielded a slower learning curve.
What problems is the product solving and how is that benefiting you?
Flagging to us when we have issues with our data builds and alerting when we have significant unexpected changes we need to take a deeper look into.
A tool with potential, but hindered by limitations currently
What do you like best about the product?
Low code/no code monitors on tables, which makes it easy to set up. Custom SQL monitors are also fairly straightforward to set up.
Allows for synergies in cases where multiple teams are using the same table for different models.
Customer support is quick to respond and acts on feedback promptly.
Allows for synergies in cases where multiple teams are using the same table for different models.
Customer support is quick to respond and acts on feedback promptly.
What do you dislike about the product?
Investigation tools for errors are very limited or maybe not intuitive
No python support so the type of checks that can be created becomes limited as well.
Lack of transparency for the machine learning thresholds and how each sensitivity level is calculated.
Dashboards not as useful/intuitive compared to something like Salesforce.
No python support so the type of checks that can be created becomes limited as well.
Lack of transparency for the machine learning thresholds and how each sensitivity level is calculated.
Dashboards not as useful/intuitive compared to something like Salesforce.
What problems is the product solving and how is that benefiting you?
The biggest problem Monte Carlo solves is transparency of data quality standards that can be viewed by anyone in the organization. For instance, we already had data validation methods that existed before Monte Carlo, but since they were owned by the teams that created the models, there was not a lot of transparency as to what data validation checks were implemented and whether they are sufficient. Monte Carlo really helps with this and makes sure the data quality is up to standard for all of our tables.
PowerFul Data Obervability Platform for Proactive Issue Detection
What do you like best about the product?
It's been a game changer for catching data issues early. We use it with Snowflake and the automated alerts for freshness, volume and schema changes save us a lot of firefighting. The data lineage view makes it easy to trace problems and the email alerts keep the team in the loop right away.
What do you dislike about the product?
Customer monitor setup can take a bit of time, and some advanced alerting needs better documentation.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us quickly detect and resolve data quality and pipeline issues before they impact downstream reporting and analytics. It provides end to end data lineage, so we can track anomalies back to the root cause across our Snowflake environments. Automated freshness, volume, and schema change alerts keep the team proactive rather than reactive, reducing downtime and improving trust in our data products.
Central tool for jobs observability and data quality
What do you like best about the product?
Features including multiple tool integrations, aggregation of assets based on multipe grouping tactics, out of the box monitors for tables which provides freshness, volume and data availability
What do you dislike about the product?
the filter available in performance and assets tab does not work as expected and often shows misleading numbers while filtering based on various tags.
What problems is the product solving and how is that benefiting you?
It helps people from Data Operations in getting holistic view of all the assets whether it is the table or jobs/pipelines from dbt, databricks and astronomer and shows the lineages. This solves the problem of looking into the same domain in cluttered way. Also, data quality and freshness monitors allows us to get realtime alerts using internal machine learning algorithms which does not require manual monitoring which ensures correct data is being fed into the database objects.
Monte Carlo Handles Simple and Complex Data Observability Needs with Relative Ease
What do you like best about the product?
Monte Carlo handles the complex data monitoring tasks and allows us to utilize our own SQL and business rules. We monitor our data by multiple segments and Monte Carlo makes that easy, alerting us when things go sideways. The Monte Carlo team also listens to us when we have ideas for improving the product (and our monitoring), and is constantly enhancing their product to meet customer needs.
What do you dislike about the product?
It's hard to pick something I really dislike about Monte Carlo. We tend to use the anomalous detection more than hard/fast rules, and there are situations where we'd like a little more control over the acceptable ranges.
What problems is the product solving and how is that benefiting you?
Monte Carlo is allowing us to automate our data monitoring, which was previously done manually. This has allowed us to expand what we are able to monitor. It also has allowed us to look at additional aspects of the data that we couldn't do with a manual process.
Monte Carlo review 05-20-2025
What do you like best about the product?
Excellent connectors for analysis and monitoring.
Lineage is very good and readable.
Support is outstanding.
API documentation is generally good and the API explorer is nice.
Simple to set up monitors and add assets.
We are using Monte Carlo extensively already given its ease of use and ability to check for anomalies.
Lineage is very good and readable.
Support is outstanding.
API documentation is generally good and the API explorer is nice.
Simple to set up monitors and add assets.
We are using Monte Carlo extensively already given its ease of use and ability to check for anomalies.
What do you dislike about the product?
Adding descriptions to objects like monitors is basically missing. It would be helpful to have a title and description field vs. having a limited description field that acts as the title as well. It is messy and requires too much curation governance. Monitors and Assets should have this capability.
Also, the APIs are ok but doc should contain better examples for use.
Also, the APIs are ok but doc should contain better examples for use.
What problems is the product solving and how is that benefiting you?
The ability to track out of range thresholds allows us to catch issues with data faster. We can resolve problems before they impact clients.
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