Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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Simple to get going, great support from the MC team
What do you like best about the product?
As a data leader, it is great to see teams across the Tech and business domains are able to onboard, self-start and build data reliability into their day to day with relatively ease. The docs, training and support from the MC team is superb, which really takes the cost and effort out of my own limited resources. And this is having measurable impact in the quality of data across our estate thanks to the automated, self-learning nature of the platform.
What do you dislike about the product?
It's a stretch for a 'downside' but... perhaps the simplicity with which we can onboard new teams into the tool and get the basics up and running means that we don't need to have dedicated focused support for the platform, which may lead to a slight plateau in our ramp up into the more mature capabilities.
What problems is the product solving and how is that benefiting you?
driving greater reliability of our data across a federated data ownership landscape of teams who are constrained for capacity and do not have 'data' as their primary purpose in their day to day.
Montecarlo as a success for business monitoring
What do you like best about the product?
Monte Carlo helped us a lot in making sense of our data quality. It is a great tool to help with catching errors in our data sources before it is identified by our stakeholders.
What do you dislike about the product?
Some features were missing from the get-go, so it took a bit of back and forth with the support to have them implemented or having workarounds for them, but they have been amazing at it.
What problems is the product solving and how is that benefiting you?
One of the biggest challenges we faced was to identify quality and quantity errors in our data pipelines. We had to create custom monitors for some of our tables to prevent errors from propagating to the end-users. Fortunately, we found Montecarlo, which helped us in monitoring and alerting us whenever there was an issue with the data.
Great data monitoring and quality tool!
What do you like best about the product?
Automatically detect and flag data anomalies in real-time at scale
Accelerate triage and troubleshoot with rich context of data assets and lineage
Intuitive and user friendly interface
Accelerate triage and troubleshoot with rich context of data assets and lineage
Intuitive and user friendly interface
What do you dislike about the product?
Incident dashboard has no filter on audience
Could receive too many Alerts if not configered property
Could receive too many Alerts if not configered property
What problems is the product solving and how is that benefiting you?
MC expedites the identification and resolution of data incidents related to freshness, volume anomalies, and schema changes. It offers comprehensive context for assets, while its data lineage capabilities enable swift tracing of incidents to their root causes, facilitating efficient troubleshooting and minimizing downstream impacts on users
Unparalleled out-of-the-box observability and ease of integration
What do you like best about the product?
The best functionality for me were the out-of-the-box ML monitors on data volume and freshness. These are hard to set up manually and they have proved crucial to use uncovering the "unknown" unknowns issues.
What do you dislike about the product?
Some functionality would benefit from more flexibility in the setup. For example, data integration errors and weekly digest emails are always sent, but we would like to adjust if and who receives them.
What problems is the product solving and how is that benefiting you?
Low data quality, unreliable data sources.
Powerful data observability tool
What do you like best about the product?
I like that that it can integrate with various tools like Airflow, DBT, Slack and that allows the team to see a fuller picture of the data quality. I also l like that everything is documented properly and appreciate the reactiveness of the Monte Carlo support team in case of question on a potentially missing feature or just how the tool works.
What do you dislike about the product?
Sometimes, the amount of features proposed can be overwhelming, as well as the UI. The feeling is "a lot is going on and I'm not sure where to look at". If one is unfamiliar with the tool or the metrics it shows, it is easy to get lost. I see however, that several video tutorial are available online to help the user naviguate the tool. Also when creating monitors, the amount of settings can be overwhelming, especially at the beginning.
What problems is the product solving and how is that benefiting you?
It shows a bigger picture of the data quality, from the source to the reporting tables. We can make different people responsible for the data quality when anomalies show up. It helps also the data source owner to be aware of the quality of the data they provide the data team with.
Our data quality watchdog
What do you like best about the product?
The ability to swiftly detect unexpected behavior in our data tables. This real-time alerting capability provides invaluable peace of mind, allowing us, data-scientists, to proactively address anomalies before they escalate into larger issues.
What do you dislike about the product?
One aspect that could be improved is the occasional occurrence of false positives. While these are to be expected to some degree in any alerting system, reducing their frequency would make the overall user experience almost perfect.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us solve our big problem: making sure our data is trustworthy every day. It keeps an eye on our data tables all the time and alerts us if something doesn't seem right. This helps us keep our data reliable and make smart decisions confidently.
Moving from User Reported Incidents to mature Data Quality Monitoring
What do you like best about the product?
Monte Carlo helps us to notice most issues with our data in key pipelines before our users become aware. This led to better decision making and an increase of trust into the data and our team. The support from Monte Carlo starting with the well guided integration, and continuing today, helps us to get the most out of this product.
What do you dislike about the product?
Data Quality must be approached with care. Monta Carlo in an incredible partner to establish a metric driven adoption. This helps us to mature in the disciplin. The only thing I would wish for is an official terraform provider instead of the embedded Monitor as Code feature that supports a slightly different feature set.
What problems is the product solving and how is that benefiting you?
Monte Carlo is helping us to notice data issues before the users are becoming aware. This enables us to fix the issue or inform our users proactively. By learning from previous incidents we can adapt our monitors and continously improve.
Data Monitoring made easy
What do you like best about the product?
1. Monte Carlo pro-actively catches data incidents that would have gone unnoticed
2. Seamless integration with other Data Tools makes it easier to track impact across platforms
3. The documentation provided guides you through the tool
4. The toolkit of checks Monte Carlo can perform makes it the leading tool
2. Seamless integration with other Data Tools makes it easier to track impact across platforms
3. The documentation provided guides you through the tool
4. The toolkit of checks Monte Carlo can perform makes it the leading tool
What do you dislike about the product?
1. Non-technical users have a bit of a learning curve
2. Out-of-the-box checks could lead to alert fatigue if unchecked
2. Out-of-the-box checks could lead to alert fatigue if unchecked
What problems is the product solving and how is that benefiting you?
Tracking breaking changes introduced by code (schema changes)
Data Irregularities: Data Volume, Update Frequency
Custom SQL Checks provide flexibility
Keeping other data-related functions in the loop for the data they own
Data Irregularities: Data Volume, Update Frequency
Custom SQL Checks provide flexibility
Keeping other data-related functions in the loop for the data they own
Great Tool for Data Observability
What do you like best about the product?
The incident feature is what I like the best. Its ability to provide real-time alerts for anomalies or issues in data ensures data reliability and integrity. This proactive approach saves valuable time and resources by quickly identifying and resolving issues before they impact operations.
What do you dislike about the product?
The UI could be more intuitive and user-friendly. It sometimes feels a bit cluttered and could benefit from a cleaner design to enhance the overall user experience.
What problems is the product solving and how is that benefiting you?
Its real-time anomaly detection saves me time.
Not too bad
What do you like best about the product?
Web interface, Rule creation, Snowflake connectivity set up
What do you dislike about the product?
Mostly, it hard to find usuful information about triggered rule
What problems is the product solving and how is that benefiting you?
Thanks to simple rules it is easy to manage snowflake monitoring across multiple geo sites.
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