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
488 reviews
from
and
External reviews are not included in the AWS star rating for the product.
Monte carlo Review
What do you like best about the product?
1.It identify any data issues quickly,
2.tracks data across pipeline
3.real time monitoring.
4.can connect to all tools
2.tracks data across pipeline
3.real time monitoring.
4.can connect to all tools
What do you dislike about the product?
1. slow interface
2. it has complex steup
2. it has complex steup
What problems is the product solving and how is that benefiting you?
1. Schema changes detaction which can scuse metadata curroption.
2. checking freshness of data wheather data is loading on time.
3. checking volume of data.
2. checking freshness of data wheather data is loading on time.
3. checking volume of data.
The tool that continuously monitors data, identify and resolving the quality issues in real-time
What do you like best about the product?
The tool helps in detecting data quality issues early and tracks data flow from source to target. It offering clear insights into dependencies and their impact on downstream systems. By enhancing data reliability, it also cuts down the time and effort required for troubleshooting.
What do you dislike about the product?
There aren't any major drawbacks, but the speed could use some improvement as it tends to be a bit slow. Also, the old UI was quite good.
What problems is the product solving and how is that benefiting you?
Monte carlo detecting the freshness,volume and schema changes of the tables. It helps us to detect and resolve the issue before the client came to us.
Mainly a great tool for observability but too technical
What do you like best about the product?
It is integrated with our main systems in the company and it also provides an intuitive interface. On a daily bases, it helps me to detect issues on the tables and views that, otherwise, I would not detect.
What do you dislike about the product?
Implementing Montecarlo has a learning curve, and it is not easy to adopt it by non-technical people. It would be good that in next versions Monte Carlo focuses on providing a site to learn more about the tool.
What problems is the product solving and how is that benefiting you?
Mainly issues in the load of tables.
Helping ease process and communication
What do you like best about the product?
Monte Carlo was very helpful in aligning and working with stakeholders to help show what kinds of issues a Data Platform team could monitor effectively, and where we would need more business involvement.
While it can be easy for engineers to scoff at queries to the information schema, the overall tool was very helpful in providing an archive of experiences and helped us build runbooks around actions taken by both senior and junior engineers.
While it can be easy for engineers to scoff at queries to the information schema, the overall tool was very helpful in providing an archive of experiences and helped us build runbooks around actions taken by both senior and junior engineers.
What do you dislike about the product?
The in built query editor and AI autocomplete tools can be a bit frustrating to work with - our folks will typically just copy from native sql tools.
I can't say this is a dislike, but rather an outcome that could have gone wrong.
The ease of set up can quickly create a deluge of new alerts - especially out of the box anomaly detection - where not everyone understands what is running or how to respond, or if stakeholders need to worry. We were fortunate to have an appropriate amount of time running in production with the teams that will use it, before bringing our few stakeholders together, and were able to determine (rather ad hoc) what was meaningful and what wasn't.
I can't say this is a dislike, but rather an outcome that could have gone wrong.
The ease of set up can quickly create a deluge of new alerts - especially out of the box anomaly detection - where not everyone understands what is running or how to respond, or if stakeholders need to worry. We were fortunate to have an appropriate amount of time running in production with the teams that will use it, before bringing our few stakeholders together, and were able to determine (rather ad hoc) what was meaningful and what wasn't.
What problems is the product solving and how is that benefiting you?
Run book creation, archive of past experiences with runbooks, alignment towards data quality types, communication within our team and with other teams around us.
Monte Carlo has been a great tool to connect data & engineering functions in a simple UI.
What do you like best about the product?
1. Very simple minimum requirements for alerts (threshold + short sql query)
2. Email / slack alerts is very neat & easy
3. Ability to search alerts by alert name & last modifided name
2. Email / slack alerts is very neat & easy
3. Ability to search alerts by alert name & last modifided name
What do you dislike about the product?
1. Not being able to search alerts by creator's name
2. Cluttered Alert UI - Notifications & Details in Alert Details section
3. Difficulty grouping alerts
4. Consistent problems with algorithimic thresholds becoming far too insensitive (including 0), and had to switch to manual thresholds.
2. Cluttered Alert UI - Notifications & Details in Alert Details section
3. Difficulty grouping alerts
4. Consistent problems with algorithimic thresholds becoming far too insensitive (including 0), and had to switch to manual thresholds.
What problems is the product solving and how is that benefiting you?
The data-product connection within my organiztion is very robust through dashboarding, reports, etc... However, there is far fewer pathways to connect the data function to engineers and make data available. Through Monte Carlo we have a simple way to allow engineers to view feature preformance (and more importantly outages), to begin approaching fixes even if an analyst isn't directly involved.
Great Tool For Automated Detection and Custom Monitors
What do you like best about the product?
The depth of the monitors is excellent. The out-of-the-box ML stuff is great and spots changes that would normally go completely under the radar. On top of that, we can set up our own custom monitors for very specific business rules we need to check. It's a great mix of automated detection and hands-on control.
What do you dislike about the product?
Since we want coverage across all our assets, the alerts we get can get pretty noisy. It feels like we're trading full coverage for a very busy channel. I think this could be improved by making the monitor configuration a bit more intuitive. It can be hard to figure out how to best set the tolerances to avoid false positives, and some in-line examples or better guides would be a huge help in reducing the noise.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us catch data quality issues in our warehouse before they blow up and impact our users. Previously, we'd often find out about a problem only after a user complained or a key report was broken. Now, we're almost always the first to know and can jump on a fix immediately. The biggest benefit has been a real boost in how much our users—and our own team—trust the data.
Great data quality monitoring/alerting tool
What do you like best about the product?
Foundational data checks available out of the box (freshness, volume) and easy to set-up custom monitors.
Lineage & Performance sections are a big plus.
Lineage & Performance sections are a big plus.
What do you dislike about the product?
Deeper integration with other tools (like airflow and DBT) would allows us to have troubleshooting sessions in one place (MC).
What problems is the product solving and how is that benefiting you?
Spot data anomalies and get alerted.
Super easy to implement and use, great monitoring for data assets and pipelines
What do you like best about the product?
Monte Carlo is ridiculously easy to use. Implementing monitoring in the datasets is a few mouse clicks and the machine learning algorithm picks up the patterns in the datasets. The default monitoring (row count, freshness monitor, query logs, schema changes) are exactly what I need for most of my datasets. It's so easy to learn to use! I've managed to implement custom sql monitoring and tests without too much consulting of the manual as MC is really intuitive. I use it in all of my datasets for daily monitoring.
The slack integration and setting up "Audiences" for any alerts is quick and easy, and I love that you can send test alerts to make sure things are working
The slack integration and setting up "Audiences" for any alerts is quick and easy, and I love that you can send test alerts to make sure things are working
What do you dislike about the product?
I'd like to be able to get alerts that output variables dependent on the issue, but that's not something I've managed so far. I haven't reached out for help though, so it could be a me issue!
What problems is the product solving and how is that benefiting you?
Monte Carlo is enabling me to monitor countless data assets, deal with problems before they affect downstream pipelines, and alerting me to issues before users have the chance! It gives me the confidence that my data products are functioning correctly and that I'll be alerted to any issues.
Great tool and concept; needs some added functionality
What do you like best about the product?
I think Monte Carlo is a great way to monitor data issues and I love the "built-in" freshness/volume anomaly monitors on any tables added to Monte Carlo.
What do you dislike about the product?
We are using the Monte Carlo product to monitor our BigQuery tables. I have chatted with Monte Carlo support about this before and put in a ticket; but it would be great if we were able to set variables within Monte Carlo monitors (we wanted to use a list within the monitor in order to take advantage of partitioning in BigQuery, as BigQuery does not support dynamic partitioning and thus a CTE would not use partitioning correctly).
Scenario:
Using a list within a Monte Carlo monitor results in failure. The MC monitor simply takes the first output written in the monitor (the result of setting the list) and considers that as the monitor. The rest of the code in the monitor (after the list is set) is not considered.
You can see the ticket or contact me for additional details/explanation.
Additionally, I think it would be useful if there were more automated monitors (for example, you could set up an automated monitor so that for ANY anomalous value in the table, the monitor is triggered).
Scenario:
Using a list within a Monte Carlo monitor results in failure. The MC monitor simply takes the first output written in the monitor (the result of setting the list) and considers that as the monitor. The rest of the code in the monitor (after the list is set) is not considered.
You can see the ticket or contact me for additional details/explanation.
Additionally, I think it would be useful if there were more automated monitors (for example, you could set up an automated monitor so that for ANY anomalous value in the table, the monitor is triggered).
What problems is the product solving and how is that benefiting you?
Monte Carlo is helping alert us to issues with data quality and freshness. It also helps the data scientists on a connected team be alerted to changes in the distribution of consumers we have purchase data for.
Data quality checks
What do you like best about the product?
The montecarlo is giving me a lot of posibilities in terms of data quality. I can setup the notifications, create a groups of people and send them a notification if something failes
What do you dislike about the product?
As the user of google chat I'm realy anoyed that I need to use emails. For me the best way for alerts will be a direct message to a google chat group. The best way will be use the webhooks that google is providing
What problems is the product solving and how is that benefiting you?
The montecarlo can compare few datasets and can send me a notification if I have less data or more. It is helping me to not make a huge mistakes for example it will send me the aler if the data from the table has been droped
showing 141 - 150