Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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Innovative tool for Data Quality Alerts
What do you like best about the product?
Monte Carlo is tool that has enhanced our data processing capabilities.
Monte Carlo is a user-friendly tool that provides comprehensive visibility into our data processing activities. It offers a clear picture of what is ACTUALLY happening with our data, enabling us to make informed decisions and optimize our processes effectively. One of the standout features of Monte Carlo is its ability to self-learn based on observations. This means that it adapts to our data, ensuring that we get the accurate and relevant insights.
The visualizations provided by Monte Carlo are easy to understand, making it simple for everyone on the team to grasp the data insights. We have the ability to configure customized monitors and alerts, tailoring the tool to our unique requirements and preferences.
Monte Carlo is a user-friendly tool that provides comprehensive visibility into our data processing activities. It offers a clear picture of what is ACTUALLY happening with our data, enabling us to make informed decisions and optimize our processes effectively. One of the standout features of Monte Carlo is its ability to self-learn based on observations. This means that it adapts to our data, ensuring that we get the accurate and relevant insights.
The visualizations provided by Monte Carlo are easy to understand, making it simple for everyone on the team to grasp the data insights. We have the ability to configure customized monitors and alerts, tailoring the tool to our unique requirements and preferences.
What do you dislike about the product?
Be prepared to be amazed about what is actually going on with data processing. The alerts can be overwhelming at first but can be customized as needed.
What problems is the product solving and how is that benefiting you?
Monte Carlo is used to monitor Data Processing and to detect issues requiring stewardship or source data issues. Integrating this into our workflows will enable faster data cleanup.
MC good for basic setups, but want to see more investment to handle advanced flows
What do you like best about the product?
- Easy to navigate UI
- Integrates well with other tools (e.g. PagerDuty)
- Automated monitoring and alerting
- Automated segmentation (so you can keep everything in 1 monitor if desired)
- Integrates well with other tools (e.g. PagerDuty)
- Automated monitoring and alerting
- Automated segmentation (so you can keep everything in 1 monitor if desired)
What do you dislike about the product?
- Anomaly detection needs improvement (e.g. wide bands, misaligned with periodic patterns, false positive alerts)
- More product development needed to handle advanced workflows (e.g. process metrics with different latencies)
- More product development needed to handle advanced workflows (e.g. process metrics with different latencies)
What problems is the product solving and how is that benefiting you?
Automated metric anomaly detection and alerting
Simple but powerful monitoring application
What do you like best about the product?
Strengths:
1. Simplicity in UI
2. Intelligent grouping of related alerts & assets
3. Performance metrics at a query level
4. Integration into Jira and other platforms
1. Simplicity in UI
2. Intelligent grouping of related alerts & assets
3. Performance metrics at a query level
4. Integration into Jira and other platforms
What do you dislike about the product?
Weaknesses:
1. Lack of visibility in certain calculations
2. No automatic updates on past alerts (for example a field for "time resolved" or "valid from")
3. Dbt lineage not consistently captured
1. Lack of visibility in certain calculations
2. No automatic updates on past alerts (for example a field for "time resolved" or "valid from")
3. Dbt lineage not consistently captured
What problems is the product solving and how is that benefiting you?
Primarily with tracking alerts and detecting anomalies. The main benefit is reducing incidents by capturing potential issues as early as possible.
Nice tool with easy api access for developers
What do you like best about the product?
Freshness, volume and validation checks are helpful for users. It has sufficient api's for developers to use Monitor as code. Building dashboards are helpful.
What do you dislike about the product?
Completeness checks need to be improved. Alerts with new message after creating incidents is not currently supported. Some of the search option for filters in the UI for assets doesn't work often
What problems is the product solving and how is that benefiting you?
Custom monitors are really helpful in finding issues. Freshness monitors detects for any failed data arrival or dag failures. Slack alerts are easy to configure and can be routed to actual team responsible for alerts
An Amazing data observability tool to add to your data ecosystem
What do you like best about the product?
Monte Carlo is incredible, providing instant value right from the start. We heavily rely on their out-of-the-box monitors, especially the frequency and volume monitors. These monitors have helped us catch numerous unexpected data anomalies that would have otherwise gone unnoticed or been discovered much later. Another great aspect of Monte Carlo is their customer service; they are highly accessible, and their response time is very quick, which helps us resolve issues faster. We have bi-weekly sessions with them where we discuss recent data mishaps and explore ways to improve. Another great aspect is that Monte Carlo continually evolves their product with new services and stays up-to-date with the latest data innovations.
What do you dislike about the product?
There isn't particularly anything that we dislike about Monte Carlo. However, I think it would be beneficial if they had a feature request page where customers could submit new feature ideas. Additionally, being able to see what new features other customers have requested could help us explore some unexplored areas of the product and utilise its full potential.
What problems is the product solving and how is that benefiting you?
Monte Carlo effectively addresses several key challenges for us. Their out-of-the-box monitors, particularly the frequency and volume monitors, are invaluable in detecting data anomalies early on. This proactive alerting has prevented potential issues, such as data bloating due to duplicates, which could have led to significant costs if left unchecked. Although alert fatigue can occur, the ability to exclude certain datasets from monitoring has helped mitigate this issue. Overall, Monte Carlo's solutions enhance our data management and operational efficiency
Montecarlo recommendation
What do you like best about the product?
Good Anamoly detection methods,easy to setup,get alerted on slack abd pagerduty on data anamolies.
What do you dislike about the product?
Detail drill down or links that could help to find the Root cause.
What problems is the product solving and how is that benefiting you?
Easy and early detection of data anamloies in the lower environments which will help to mitigate the issues in production
Useful data checks
What do you like best about the product?
- Variety of check types
- Intuitive UI
- Option to build monitors as a code
- Intuitive UI
- Option to build monitors as a code
What do you dislike about the product?
- Notifications cannot be customized
- Custom metrics cannot be scheduled via cron
- Custom metrics cannot be scheduled via cron
What problems is the product solving and how is that benefiting you?
Automation of data quality checks to have a better visibility of data issues if any.
Interesting product but needs a lot improve
What do you like best about the product?
1. The result visualization is useful.
2. The customized query is super helpful when we need to design some complicated alerts.
3. The yaml generation function in UI is also helpful so we can make sure the new code can always be in the correct new format.
2. The customized query is super helpful when we need to design some complicated alerts.
3. The yaml generation function in UI is also helpful so we can make sure the new code can always be in the correct new format.
What do you dislike about the product?
1. ML thresholds do not work well. We are missing lots of important alerts, just because the thresholds go really wide and we are not aware of the issue at all.
2. The MaC keeps on changing, the definition, the structure, the scope, everything keeps on changing frequently, we have to keep on changing our code, which is super annoying.
3. Why do you decide to remove the freshness monitor from MaC and have to let us manually add in a weird other notification place? I do not get the design now. It makes things chaotic.
4. Sometimes dry run passed but after merging the PR, the apply fails. I feel there are some inconsistency there and it is really confusing.
2. The MaC keeps on changing, the definition, the structure, the scope, everything keeps on changing frequently, we have to keep on changing our code, which is super annoying.
3. Why do you decide to remove the freshness monitor from MaC and have to let us manually add in a weird other notification place? I do not get the design now. It makes things chaotic.
4. Sometimes dry run passed but after merging the PR, the apply fails. I feel there are some inconsistency there and it is really confusing.
What problems is the product solving and how is that benefiting you?
We are able to keep on monitoring some important BQ tables.
Montecarlo feedback
What do you like best about the product?
Ease of Use and the table/field lineage is very helpful along with the refresh time, alerts, row change and lot of areas were covered.
Integration is very easy.
Integration is very easy.
What do you dislike about the product?
Sometimes the incorrect data was shown which is little misleading
What problems is the product solving and how is that benefiting you?
row count change, table and field lineage and the custom alerts to notify the change of data.
The definitive way to monitor your data for anomalies
What do you like best about the product?
Monte Carlo makes it really easy to create monitors and alert your team when something triggers your monitors. There is a thorough edit history and ways to test your monitors.
What do you dislike about the product?
The main downside of Monte Carlo is sometimes not having the easiest way to know if you wrote your queries correctly for your monitors. This is mostly a user training issue though, but there are AI tools that help you too.
What problems is the product solving and how is that benefiting you?
Understanding when unexpected behavior in our systems are happening
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