Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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Great Data Observability & Quality tool !
What do you like best about the product?
Montecarlo assures us that data is fresh and accurate. Montecarlo notifies us on Slack when there is an incident. We were able to spot data sync issues thanks to Montecarlo. We can create custom monitors to monitor a specific rule. Montecarlo is easy to use and the product is always evolving, adding new features. We can monitor easily the dbt runtimes for each model, the volume of queries on our database ... Also the MC team is very available and is quickly answering to our questions. I'm using MC every day and it makes my work easier !
What do you dislike about the product?
If I had to say something it would be the fact that sometimes an incident is a group of incidents on different tables, and that sometimes the incidents are not related, so we should not have only one status, but one status by incident. Also MC is sometimes mixing up two tables with the same name but in different schemas. Also the lineage feature doesn't seems to work from time to time.
What problems is the product solving and how is that benefiting you?
Monitor the quality, accuracy, freshness of the data for us. Lot of time gained and serenity.
My Experience of using MonteCarlo as a Data Engineer
What do you like best about the product?
Catching unusual variations in table over time
What do you dislike about the product?
False positive in volume monitor.
Time needed to set-up custom monitors
Time needed to set-up custom monitors
What problems is the product solving and how is that benefiting you?
Catching errors in tables and making sure our tables are always reliable
Monte Carlo Review
What do you like best about the product?
- Easy to get an end-to-end view of data flows in our warehouse
- Great on boarding support
- Each user in our company can use it
- New features appearing
- Some support for Data products
- Great on boarding support
- Each user in our company can use it
- New features appearing
- Some support for Data products
What do you dislike about the product?
- Not completely clear how to use it with DBT and some other tools.
- API docs HTML page causes browser to freeze because they're too long
- API docs HTML page causes browser to freeze because they're too long
What problems is the product solving and how is that benefiting you?
Giving me end-to-end observability in BigQuery including simple lineage and column level lineage
A very effective data observability product with good customer support
What do you like best about the product?
One of the best upsides of Monte Carlo for me is that it catches data incidents you didn't think to check for with explicit tests in your pipeline, tightening feedback loops and enabling you to reduce your time to recovery.
The lineage feature is also very useful to us, and the fact that everything can be done via APIs makes it very engineer-friendly.
The lineage feature is also very useful to us, and the fact that everything can be done via APIs makes it very engineer-friendly.
What do you dislike about the product?
The integration with git for Looker dashboard lineage has been the most painful part for us, as we have many LookML github repos with distributed ownership, so tracking down the admins of those and getting them to set up deploy keys has been a laborious process.
What problems is the product solving and how is that benefiting you?
It provides monitoring coverage for unknown-unknown situations, and we hope that this combined with the incident handling capability will help use reduce time to recovery for data incidents.
The lineage information it gathers isn't just limited to particular pipelines or one database, the fact that it can also incorporate Looker dashboards makes it very convenient for us and helps us to understand and surface the data supply chain behind reports, etc.
The lineage information it gathers isn't just limited to particular pipelines or one database, the fact that it can also incorporate Looker dashboards makes it very convenient for us and helps us to understand and surface the data supply chain behind reports, etc.
Comprehensive solution for ensuring data reliability, freshness and accuracy
What do you like best about the product?
The most helpful about Monte Carlo is the automated monitoring functionality detecting real-time on freshness issues, volume anomaly and alert users promptly. Customer support is top notch as well.
What do you dislike about the product?
Not really any downsides on the provided functionality.
What problems is the product solving and how is that benefiting you?
It ensures data reliability in our data pipelines and reduce data downtime caused by issues such as pipeline failures or infrastructure problems.
Monte Carlo personnel experience
What do you like best about the product?
Monte Carlo is user friendly and of great utility when table monitoring is needed.
What do you dislike about the product?
Past related anomalies need to be simpler to view.
What problems is the product solving and how is that benefiting you?
Easily detect bad data before the jobs fails.
Effective data quality management
What do you like best about the product?
Customizations options on setting up data quality check alerts.
Effective tracking of incidents updates via comments section
Effective tracking of incidents updates via comments section
What do you dislike about the product?
No incident number to search for or track.
Multiple tables getting merged into single incident, often leads to confusion
Multiple tables getting merged into single incident, often leads to confusion
What problems is the product solving and how is that benefiting you?
We are able to catch data discrepancy pretty early in the game which is preventing data loss/discrepancy situations.
review monte carlo
What do you like best about the product?
helped us find the bug in our code that has improved product
What do you dislike about the product?
Not really, has worked well for our teams. perhaps too many alerts
What problems is the product solving and how is that benefiting you?
helps us find issue in data quality
Enhances Data Quality Monitoring with ML and Slack
What do you like best about the product?
I like how Monte Carlo brings data quality insights to the people who can fix them, the users of the data sources. I also find the ML thresholds helpful because they let Monte Carlo handle the error alerts, so the data platform team doesn't have to create the error thresholds manually. The integration with Slack is another plus, as it offers a centralized place for alerts and makes it easy to send them to the right stakeholders. Monte Carlo is easy to use, even though I didn't handle the initial setup.
What do you dislike about the product?
I'm having challenges with integrating Monte Carlo with AI agents. It would be great if AI agents could interact more seamlessly with Monte Carlo.
What problems is the product solving and how is that benefiting you?
Monte Carlo brings data quality insights to users who can fix them. It handles error alerts with ML thresholds so the data platform team doesn't have to set them. Slack integration centralizes alerts and sends them to the right stakeholders.
Monte Carlo finds the needle in a haystack
What do you like best about the product?
1) Ease of set-up - Out-of-the-box monitors for freshness, volume, and schema start training as soon as the MC service principal has access to the tables.
2) Effective Visuals – Results and Incident are visually appealing, but most importantly they present the data in a way that can be easily consumed. The percent increase or decrease for a metric is presented over days or weeks aside the row counts to give context.
3) Seamless Slack integration – It is quick to set-up MC monitor notifications via Slack channels. Responses written in Slack are saved to Monte Carlo, saving time and effort.
4) Low noise - MC keeps the noise level down. False-positive issues are not as common as found in other tools.
5) Customer support - MC hands-down has the best customer support. Their team feels part of our own. They have learned the nuances of our data. They respond quickly to all questions and concerns. Suggestions that we have made about the tool have been implemented (quickly). They are constantly evolving the tool to make it even better.
2) Effective Visuals – Results and Incident are visually appealing, but most importantly they present the data in a way that can be easily consumed. The percent increase or decrease for a metric is presented over days or weeks aside the row counts to give context.
3) Seamless Slack integration – It is quick to set-up MC monitor notifications via Slack channels. Responses written in Slack are saved to Monte Carlo, saving time and effort.
4) Low noise - MC keeps the noise level down. False-positive issues are not as common as found in other tools.
5) Customer support - MC hands-down has the best customer support. Their team feels part of our own. They have learned the nuances of our data. They respond quickly to all questions and concerns. Suggestions that we have made about the tool have been implemented (quickly). They are constantly evolving the tool to make it even better.
What do you dislike about the product?
We have many custom monitors. We can effectively see and manage these monitors in the Monte Carlo tool. On our wish list, we would also like to see these monitors by schedule time.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us identify bugs and data freshness issues quickly, enabling teams to fix the problems faster. Having a quicker turnaround saves the company money by having less data loss and backfills. Better data equals better decisions.
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