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Monte Carlo is a reliable product
What do you like best about the product?
Data observability and data reliability delivered
What do you dislike about the product?
More security features should be added with regards to data safety.
What problems is the product solving and how is that benefiting you?
Major problem solved by Monte Carlo is data break which enaures data recovery and end to end data observability.
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It Makes You Trust Your Data
What do you like best about the product?
It's never been easier to truly trust and understand your data.
I find it super helpful in every aspect of managing a data warehouse.
- Alarming when something is wrong
- Understand how the tables are tied together
- The impact of schema changes
- Cleanup and other supportive tools
There's also great support and the platform keeps improving all the time
I find it super helpful in every aspect of managing a data warehouse.
- Alarming when something is wrong
- Understand how the tables are tied together
- The impact of schema changes
- Cleanup and other supportive tools
There's also great support and the platform keeps improving all the time
What do you dislike about the product?
Used tobe the UI, setting up multiple monitors , but it has already been improved by a lot.
What problems is the product solving and how is that benefiting you?
It helps me be on top of every data issue we may encounter, no matter how bizarre or edge-case it may be.
Great product with amazing customer support
What do you like best about the product?
Monte Carlo has such a clean interface and makes onboarding so easy. It is extremely useful for doing data validation ensuring that our critical data does not have any discrepancies. They also have the best support team! They immediately reached out to try and find a solution to my problem. Awesome product!
What do you dislike about the product?
One small suggestion would be to make the created rules more easily searchable. Otherwise love using the product.
What problems is the product solving and how is that benefiting you?
Instead of needing to build batch jobs to run validation checks against our data, we can simply create sql scripts to check the data. This makes it easy to fix the validation if the logic is wrong and to add new comprehensive checks.
An great tool to ensure data quality and speed-up problem resolution
What do you like best about the product?
As data scientists/engineers, we spend time fixing data pipelines.
Monte Carlo data monitoring solution provides us a big help though:
- a complete and near real-time overview of the health of our data architecture
- an easy-to-use and intuitive platform
- possibilities to custom the dashboards based on specific business needs
- reactive alerts through notifications
Monte Carlo data monitoring solution provides us a big help though:
- a complete and near real-time overview of the health of our data architecture
- an easy-to-use and intuitive platform
- possibilities to custom the dashboards based on specific business needs
- reactive alerts through notifications
What do you dislike about the product?
Nothing much to say. It could miss the possibility to connect to some channels for notifications (depending on your need) and maybe a better way to get rid of some false alerts.
What problems is the product solving and how is that benefiting you?
It allows us to get a quick and visual overview of the status of our data-driven solutions, and especially the freshness of the data.
We save precious time in searching for the root cause when something breaks.
It also helps to increase trust from the end-users.
We save precious time in searching for the root cause when something breaks.
It also helps to increase trust from the end-users.
Tool we were waiting for
What do you like best about the product?
Being in the data field for a long time, you know how important data quality is. People will use data only if they trust it. We created multiple monitors in our pipelines, as well as a variety of data checks, which need to be maintained and supported. With Monte Carlo we have a tool that helps to find various data problems using AI underneath as out-of-box solution. The time for RCAs and problems resolution decreases dramatically, which saves lots of resources.
Besides AI driven notifications we can create business-defined custom monitors to have a complete picture about data health.
Besides AI driven notifications we can create business-defined custom monitors to have a complete picture about data health.
What do you dislike about the product?
No able to observe RDBMS and Kafka so far.
What problems is the product solving and how is that benefiting you?
Data lineage helps to find the root cause of the problem and, as a result, much faster resolution. Metadata aggregated by Monte Carlo and shared with us gives valuable insight into the data infrastructure and abilities to improve it.
The future of data observability
What do you like best about the product?
With the growing complexity of data pipelines, MC helps navigate the data flow to and from our databases.
It is, in many cases, the first tool I would use to identify the source of a specific data piece (even up to a single column).
Every new feature or update seems to solve problems I didn't even think about and is always helpful in pinpointing the origin of issues.
Customer success is super helpfull and proactive in any engagment.
Setting up monitors on tables is done with a simple press of a button and it is very easy to follow up on alert status.
I can't imagine going back to a data environment without MC.
It is, in many cases, the first tool I would use to identify the source of a specific data piece (even up to a single column).
Every new feature or update seems to solve problems I didn't even think about and is always helpful in pinpointing the origin of issues.
Customer success is super helpfull and proactive in any engagment.
Setting up monitors on tables is done with a simple press of a button and it is very easy to follow up on alert status.
I can't imagine going back to a data environment without MC.
What do you dislike about the product?
On rare occasions, there are some minor UI issues, but those are resolved very quickly.
Aside from that, nothing much bothers me.
Aside from that, nothing much bothers me.
What problems is the product solving and how is that benefiting you?
With MC's plug-and-play monitoring, we gained the ability to detect issues across the entire warehouse without having to set up alerts manually for every single table.
Also, having the ability to pinpoint the cause of an issue with just a few simple steps saves a ton of time and effort.
It further helps when non-engineers can now explore the pipelines without the help of the person who set the pipeline up.
Also, having the ability to pinpoint the cause of an issue with just a few simple steps saves a ton of time and effort.
It further helps when non-engineers can now explore the pipelines without the help of the person who set the pipeline up.
Monte Carlo is helping our team build trust in the quality of our data
What do you like best about the product?
Monte Carlo's out-of-the-box anomaly detection saved our team an immense amount of time and helped us catch data quality issues before they became reporting issues. That has helped our entire team feel more confident in our data.
What do you dislike about the product?
Our team would like to be able to customize the messages and the routing of the alerts with a little more nuance and context. The MC team has already implemented multiple features to try to help with this so I am confident that in a few months this will no longer be a gap.
What problems is the product solving and how is that benefiting you?
Our team is trying to reduce distrust in our data systems. That distrust manifests itself in ad-hoc requests to our engineering teams or bug-fix requests asking just to track down bad data at the source. Having end-to-end observability of our data has given our team the ability to monitor the whole pipeline so they can systematically build trust in our systems.
An immediate leap forward in data observability
What do you like best about the product?
The out-of-the-box anomaly detection on freshness and volume provides immediate value. The Slack notifications are very understandable and provide an easy way of linking back to specific incidents in the Monte Carlo UI. Additionally, the lineage capabilities are very useful when identifying the downstream impacts of any incidents. Monte Carlo has also been extremely receptive to feedback and provided timely suggestions and updates as appropriate. Overall the product impact has been extremely positive, and it has been a pleasure working with their team.
What do you dislike about the product?
The routing of notifications based on different types of alerts is not as granular as would be ideal, but they have been receptive to that feedback and have indicated there will be improvements coming in that area.
What problems is the product solving and how is that benefiting you?
The #1 problem that we are solving is timely awareness of any data issues; the combination of automated anomaly detection, customized rules and notification routing provides an effective way of configuring various alerts. An additional benefit is the data lineage visibility - this was not our primary reason for implementing Monte Carlo but has been very useful in tracing the impact of incidents.
MC really helps your data quality
What do you like best about the product?
- it monitors and alerts on all your data warehouse tables out-of-the-box
- transparent and diligent customer success and engineering teams
- easy to incorporate into your day-to-day operations (especially if you use Slack alerts)
- Monte Carlo has caught many critical issues before customers noticed (on top of uncovering silent data issues in our data warehouse)
- transparent and diligent customer success and engineering teams
- easy to incorporate into your day-to-day operations (especially if you use Slack alerts)
- Monte Carlo has caught many critical issues before customers noticed (on top of uncovering silent data issues in our data warehouse)
What do you dislike about the product?
Monte Carlo is always accepting feedback from their clients and actively improving their products. If I list a few minor UI issues today, the Monte Carlo team will probably resolve these by the time you are reading this. I'd recommend being open and sharing your feedback directly with the Monte Carlo teams.
What problems is the product solving and how is that benefiting you?
We had a large data warehouse with limited data observability (we only had data validations and alerts for a selected few critical pipelines). By adding in Monte Carlo, we gained data observability on all other data sets without sacrificing months of data engineering development work. Monte Carlo has helped improve trust in our data and pass on a sense of data ownership to data consumers and producers.
Great Out of the Box Functionality with Really Bright Future
What do you like best about the product?
- The ML powered anomoly detection provide great low effort reliability checks.
- The lineage feature is great building block mapping data lifecycle and determining impact.
- Incident IQ page provides an really helpful interface for working data incidents and tracking progress.
- Slack integration allows for quick triage and resolution use cases.
- The customer success team is super accessible and a pleasure to work with.
- The entire team from CEO down is really helpful and super interested in partnering with customers to make the product better.
- The GraphQL API is really easy to use and provides a great starting point for extending the product.
- The lineage feature is great building block mapping data lifecycle and determining impact.
- Incident IQ page provides an really helpful interface for working data incidents and tracking progress.
- Slack integration allows for quick triage and resolution use cases.
- The customer success team is super accessible and a pleasure to work with.
- The entire team from CEO down is really helpful and super interested in partnering with customers to make the product better.
- The GraphQL API is really easy to use and provides a great starting point for extending the product.
What do you dislike about the product?
- I would like to see more automation around the Incident IQ feature to be on par with other incident management tools like Datadog, Pagertree, and Rootly.
- More first-class support for dbt, Prefect/Airflow, Fivetran, Kafka. However, adding these via the lineage API is possible and something we do.
- SDK/CLI for creating MC objects/monitors in code. However, building this internally via the monitors API is possible.
- Catalog feature needs some updating to be on par with companies focused on that feature.
- More first-class support for dbt, Prefect/Airflow, Fivetran, Kafka. However, adding these via the lineage API is possible and something we do.
- SDK/CLI for creating MC objects/monitors in code. However, building this internally via the monitors API is possible.
- Catalog feature needs some updating to be on par with companies focused on that feature.
What problems is the product solving and how is that benefiting you?
- Automated data quality monitoring and notifications
- Data lineage to troubleshoot issues, plan changes, and document full data lifecycle
- Snowflake variant schema change monitors
- Data incident management
- Custom monitors as needed
- Data lineage to troubleshoot issues, plan changes, and document full data lifecycle
- Snowflake variant schema change monitors
- Data incident management
- Custom monitors as needed
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