Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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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. I find it easy to navigate especially and track down the most important models. There is a feature that let's you know if a query has changed based on the number of characters in a query, which is really nice.
What do you dislike about the product?
I really wish there was a way to snooze the monitors and alerts in the same manner, as it can sometimes become overwhelming.
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.
Full Quality Coverage
What do you like best about the product?
What I like most about Monte Carlo is that it raises all data issues in the system. No anomaly goes undetected.
Also, the ability to view table and field lineage is very helpfull.
Also, the ability to view table and field lineage is very helpfull.
What do you dislike about the product?
Monte Carlo can provide many false alerts. User has to fine tune the algorithm in order for Monte Carlo to yield better results.
What problems is the product solving and how is that benefiting you?
Monte Carlo provides ongoing built in data quality monitoring with volume and freshness anomalies. In addition you can create custom monitors to better suite specific needs.
In cases where data issues are raised by Monte Carlo you can use the linage tool to better know which models and reports were affected by this issue.
In cases where data issues are raised by Monte Carlo you can use the linage tool to better know which models and reports were affected by this issue.
Monte Carlo - our eyes on the data piplines.
What do you like best about the product?
Real easy to use product , focuses on the right areas , uses AI / ML for recommendations and tries to find the right balance between alerting and reducing noise.
What do you dislike about the product?
more connectivity to our current ETL tool to complete the whole picture.
What problems is the product solving and how is that benefiting you?
Finds us data issues that we will never see before our reports / dashboard users.
Great tool for Enterprise Data Observability
What do you like best about the product?
The built-in machine learning monitors that track freshness, volume, and schema changes are fantastic. I really appreciate how these features work right out of the box.
What do you dislike about the product?
To be completely honest, this is the best tool I have used for data observability and large-scale data quality checks. However, if I had to mention one drawback, it would be the extra features that come with the integrations. For example, MC attempts to display traces from our Airflow integration in several areas, but I have noticed that the information is not always accurate in some places. I have observed a similar issue with the dbt integration as well.
What problems is the product solving and how is that benefiting you?
This is one of my favorite technology I have ever used. I really love it's out-of-the-box ML monitors that provide us alerts whenever an anomaly is detected and in majority of the cases it's a true positive. Data quality is critical for any organization and being able to manage it across the organization without spending a lot of time on it is something really great. Monte Carlo empowers us to do this in the most efficient and optimized way. It has a wide range of standard monitor templates using which we can quickly create table monitors and also provides customization to the level where we can define monitors using YAML code! It's helping us detect any data quality issues very quickly and also provides a nice lineage and the impact analysis.
Nice and useful software
What do you like best about the product?
I think the alerts are really valuable. Also the summary of the monitors give you a good understanding of what is happening with your tables. I use a lot the table lineage feature when I'm looking at a new table, it is easy to understand the dependencies of it, and how important it is.
What do you dislike about the product?
When I see the summary page, I can only see the last 30 days of the monitors' history. It is helpful but I'd really like to have more history, so I can better understand the incidents and check when some trends started.
Also, I activated the field health monitors for some important tables; however, the it is sending a lot of false positives alerts, generating alert fatigue
Also, I activated the field health monitors for some important tables; however, the it is sending a lot of false positives alerts, generating alert fatigue
What problems is the product solving and how is that benefiting you?
It makes it easy to have a general look of the tables, and it also automates the monitoring so that you can be safe knowing that if anything strange happens, you'll have Montecarlo to alert you.
I’m a BI Developer in MoonActive, using Mone Carlo to observe my company’s data
What do you like best about the product?
I’ll say few positive things:
1. The UI is very good, easily can create custom alerts and to investigate the data
2. The ability to connect MC with many alerts pipes such as Slack, mail
3. The sensitivity feature that allow us as data users to control when MC alerts will take action
1. The UI is very good, easily can create custom alerts and to investigate the data
2. The ability to connect MC with many alerts pipes such as Slack, mail
3. The sensitivity feature that allow us as data users to control when MC alerts will take action
What do you dislike about the product?
1. Sometimes the default alerts sensitivity is too high, and then I get spam alerts (for example added 10K rows, usually 10.5K rows). I’ll prefer that the default will be less sensitive
2. The custom alerts title to Slack requires 1 row, which requires aggregation of the query into a single row. It would be more convenient if MC would already take the column and collapse it into a single row automatically w\o using SQL function
2. The custom alerts title to Slack requires 1 row, which requires aggregation of the query into a single row. It would be more convenient if MC would already take the column and collapse it into a single row automatically w\o using SQL function
What problems is the product solving and how is that benefiting you?
1. Automatic monitoring of my DB even when new tables are added without the need for manual intervention
2. The ability to automatically identify anomalies in the data
3. The alerts are dynamic - important tables will be marked with a star, data that has been sorted out will be marked with ״normalized״. This really helps me pay attention and emphasize important things
2. The ability to automatically identify anomalies in the data
3. The alerts are dynamic - important tables will be marked with a star, data that has been sorted out will be marked with ״normalized״. This really helps me pay attention and emphasize important things
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