Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
0 AWS reviews
-
5 star0
-
4 star0
-
3 star0
-
2 star0
-
1 star0
External reviews
486 reviews
from
and
External reviews are not included in the AWS star rating for the product.
Easy to use observability tool
What do you like best about the product?
Non technical people can start using it with just basic sql knowledge.
What do you dislike about the product?
The commercial model that pushes you to pay for assets you do not necessarily want to track.
What problems is the product solving and how is that benefiting you?
It allows you to track your data assests with very low effort.
Administering the MonteCarlo tool in a Pharmaceutical company
What do you like best about the product?
- customer support is highly responsive, active and eager to help
- programmatic resources like Monitors as Code, API that enable plugging MonteCarlo in CI/CD pipelines
- graphical representation of incidents, data flows and patterns
- data products feature
- programmatic resources like Monitors as Code, API that enable plugging MonteCarlo in CI/CD pipelines
- graphical representation of incidents, data flows and patterns
- data products feature
What do you dislike about the product?
- documentation on the API usage around getting the results of monitors, triggering the monitors programmatically could have more examples
What problems is the product solving and how is that benefiting you?
Team that I work with is able to easily apply the data quality metrics and spot the data anomalies or data flow incosistencies way easier than before.
Great solution for data observability
What do you like best about the product?
- The tool overall works great. Offered features are enough to cover wide spread of use-cases.
- The team is also great. We've always had very quick response to our support requests.
- Automation features are the best. We've set up almost everything using code, which makes resources easily manageable.
- They are very open for feature requests and deliver them relatively quickly. We requested monitoring support nested fields and structs, which was implemented within weeks.
- The team is also great. We've always had very quick response to our support requests.
- Automation features are the best. We've set up almost everything using code, which makes resources easily manageable.
- They are very open for feature requests and deliver them relatively quickly. We requested monitoring support nested fields and structs, which was implemented within weeks.
What do you dislike about the product?
- We had some headache during Databricks integration, which was mostly caused by our platform not being Unity enabled yet. But with support, we've dealt with all the issues.
- There is a blocklist feature which works on schema level. It would be great if we could block table level access with that feature.
- There is a blocklist feature which works on schema level. It would be great if we could block table level access with that feature.
What problems is the product solving and how is that benefiting you?
We're leveraging its anomaly detection capabilities to great extend. Almost all our pipelines and tables are monitored for various use-cases. It works much better than some alternatives we've used.
Best Data Observability Platform
What do you like best about the product?
i like the way how MonteCarlo provides standard out-of-the box monitors, simple -no frills - UI to setup, the way how monitors, alerts are integrated with Slack, Collaboration from Product team on day to day basis in slack, promptness in response, knowledgable product managers & periodic sync up with them, Incident management, data sharing
What do you dislike about the product?
There is a period initially when we got bogged down by alert fatigue & few monitor setup options that were maturing. Those have been eliminated as of now
What problems is the product solving and how is that benefiting you?
near real-time Data Quality checks on the data, immediate alerting mechanisms in Colloboration channel, ability to create domains & have business users self-service - without losing the overall observability & control on the environment.
Great tool for Data Governance with space for improvements
What do you like best about the product?
I really like the integration with airflow, allowing to identify tasks that load specific tables.
The monitors are also very useful and the possibility of identifying domains and personalize the alerts for each of them.
The customer support is really good and fast.
The monitors are also very useful and the possibility of identifying domains and personalize the alerts for each of them.
The customer support is really good and fast.
What do you dislike about the product?
I am not so confident about the field lineage, specially when copying data, we have faced sometimes the disruption of the lineage due to that.
What problems is the product solving and how is that benefiting you?
Is spotting any issue with data really fast allowing us to fix some problems before business even noticing that.
Great combination of data observability and data quality
What do you like best about the product?
1. the ease of setting up and understanding the monitors
2. Great variety of audiences and the ability to configure custom messages that includes elements from your actual query
3. The observability part is extremely powerfull. It observed all irregularities that were reported by users in ServiceNow plus an additional set of findings
4. It's good to see that Monte Carlo develops in an iterative way. You see the application continously changing, mostly for the better while the basic functionality stays in plays.
2. Great variety of audiences and the ability to configure custom messages that includes elements from your actual query
3. The observability part is extremely powerfull. It observed all irregularities that were reported by users in ServiceNow plus an additional set of findings
4. It's good to see that Monte Carlo develops in an iterative way. You see the application continously changing, mostly for the better while the basic functionality stays in plays.
What do you dislike about the product?
Monte Carlo is not yet really suited to work with many from multiple teams. People from different teams can see and report on each others monitors, which is good, but it's hard to create with one mouse click an overview of all monitors and incidents for which a particular team is responsible for. We misuse a combination of domain and audience to achieve this plus I appointed myself as Monte Carlo data steward to enforce that users stick to a naming convention for monitors and dashboards
What problems is the product solving and how is that benefiting you?
1. Providing a stable, easy to maintain platform to store and run SQL business rules that have been defined by data stewards working in staff departments in our organization. We report the data quality in a Qlik dashboard
2. Perform pro-active data observability where we want to move away from a situation where our data users report about data issues to a situation where we can pro-actively inform our data users about data issues
2. Perform pro-active data observability where we want to move away from a situation where our data users report about data issues to a situation where we can pro-actively inform our data users about data issues
great tool and service
What do you like best about the product?
how the ML evolve as more information is provided.
the lineage
the lineage
What do you dislike about the product?
sometime it can be a little noisy.
there is an opportunity for this to be used as a catalog but it is not as user friendly for that.
no s3 check
there is an opportunity for this to be used as a catalog but it is not as user friendly for that.
no s3 check
What problems is the product solving and how is that benefiting you?
giving headsup if incorrect or incomplete data
Great tool to monitor the data quality!
What do you like best about the product?
Monte Carlo is very easy to onboard and user-friendly. Setting up data quality monitors is straightforward, allowing us to detect problems right away in case of any breaches. Alerts can be sent to respective Slack channels, making the appropriate people aware. Since adopting Monte Carlo, the data quality governance capabilities in our company have increased tremendously!
What do you dislike about the product?
There are many types of monitors, which can be confusing at times regarding the conditions under which to use them.
What problems is the product solving and how is that benefiting you?
As a rapidly growing company, we generate a vast amount of data and require timely monitoring to swiftly detect and inform the right people to address any issues. Monte Carlo enables us to set up all the monitors in one place and view data quality problems at a high level. Any specific breaches are sent to a Slack channel and resolved by the respective individuals.
Data Observability Platform that Delivers
What do you like best about the product?
Getting value with minimal cusom work
Ease of Integration and implementation
Customer support team are great and always assisting and answering our inquiries
Ease of Integration and implementation
Customer support team are great and always assisting and answering our inquiries
What do you dislike about the product?
Need a bit more controls over the ML model sensitivity to reduce manual work of creating custom freshness/volume monitors
Would appriciate a bit more ease of use on the platform management capabilties (e.g. managing dataset and table monitoring)
Would appriciate a bit more ease of use on the platform management capabilties (e.g. managing dataset and table monitoring)
What problems is the product solving and how is that benefiting you?
Moving our data engineering team to be much more proactive when it comes to finding and fixing data and data pipelines related issues
Get back the trust of your stakeholders!
What do you like best about the product?
As a data analyst, one of the standout features that I absolutely love is its ability to proactively detect and alert on data issues, and fix them before the stakeholders send you on Slack "something seems off with the numbers".
Moreover, the platform's intuitive interface deserves a special mention. It simplifies complex data lineage, making it easy to trace the journey of every piece data model. The visualizations provide a clear picture of dependencies, helping to understand the impact of any changes.
The Customer Support/onboarding team are very reactive and are always here to help. Always available to help or even to configure any alerts with you based on your use-cases. The integration of the tool also went quite smoothly as the onboarding was very complete and structured from A to Z.
Moreover, the platform's intuitive interface deserves a special mention. It simplifies complex data lineage, making it easy to trace the journey of every piece data model. The visualizations provide a clear picture of dependencies, helping to understand the impact of any changes.
The Customer Support/onboarding team are very reactive and are always here to help. Always available to help or even to configure any alerts with you based on your use-cases. The integration of the tool also went quite smoothly as the onboarding was very complete and structured from A to Z.
What do you dislike about the product?
Honestly, not much. The learning curve for new users might be a bit steep, especially for those not familiar with data observability problems. I would say that the benefits far outweigh any minor drawbacks.
What problems is the product solving and how is that benefiting you?
The tool helps organizations detect and alert on data anomalies in real-time, allowing data teams to identify and address issues before they impact business decisions. This is crucial for maintaining trust in the data, as inaccurate or unreliable information can lead to flawed analyses and misguided decision-making.
showing 351 - 360