Monte Carlo works as a centralized data tool. We use it for data observability and anomaly detection, which helps identify issues and changes in data flows.
Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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Seamless and invaluable level of insight observability to focus on enrichment and innovation.
What do you like best about the product?
Being part of the Data Team, MC is getting us timely intel on both edge case issues and more common BAU ones that crop up from time to time.
MC alerting identifies what it believes to be out of the ordinary coupled with it's in-built lineage tool reducing our time spent on investigation and quicker resolutions.
A direct consequence of this being the number of data related queries from data consumers in our business has significantly dropped.
MC alerting identifies what it believes to be out of the ordinary coupled with it's in-built lineage tool reducing our time spent on investigation and quicker resolutions.
A direct consequence of this being the number of data related queries from data consumers in our business has significantly dropped.
What do you dislike about the product?
There are no dislikes from me on the MC product as a whole and it is evolving so looking forward to whats to come. There is a lot of information available and thats certainly not a bad thing, one tiny nit would be a way to group issues from multiple pipeline streams which are related.
What problems is the product solving and how is that benefiting you?
Timely intel for key Data Warehouse tables e.g
- Unusually stale tables which can be from 3rd party syncing functions issues
- Alerting on duplicate keys in reporting tables
- Table schema changes
With this sort of visibility we have better control over our data which depends on many different pipelines, consolidating all that reporting and alerting is invaluable.
- Unusually stale tables which can be from 3rd party syncing functions issues
- Alerting on duplicate keys in reporting tables
- Table schema changes
With this sort of visibility we have better control over our data which depends on many different pipelines, consolidating all that reporting and alerting is invaluable.
Easy to use!
What do you like best about the product?
The monitors as code YAML configuration is great for managing our monitors, and creating reusable configurations. Customer support have also been great with any issues or questions we've had.
What do you dislike about the product?
More integrations with our existing observability tools such as DataDog and Grafana would be great.
What problems is the product solving and how is that benefiting you?
We have a lot of data created by many different teams and Monte Carlo is helping us build confidence and trust in our datasets so that we can use them for critical functions as well as for other teams to use for analysis.
Useful insights about our data straight out of the box
What do you like best about the product?
It's very easy to connect and set up some monitors off the shelf which provide very helpful observations about our data. These have already helped us identify and address some issues with very little effort on our part.
What do you dislike about the product?
At times it's obvious that Monte Carlo is still quite a young product. For example the audiences/notifications section is a little clunky, it's sometimes hard to replicate functionality that's in the web console in the CLI, and we had some issues where monitors failed to run because of a configuration error but they weren't notifying us of the failure. However the team at monte carlo have been very receptive to feedback and quickly made product improvements in response.
What problems is the product solving and how is that benefiting you?
Identifying issues in our data
The Monte Carlo platform speaks for itself - useful data anomaly detection from day 1
What do you like best about the product?
The most impactful benefit of using Monte Carlo is the detection of anomolies that would otherwise be near impossible to have visibility of. Onboarding is straightforward and you can realise benefits from day 1 with very little development effort. The product as a whole is well thought out and gives both the technical and non-technical user feature rich functionality. The low effort integrations to messaging platforms like Slack and workload management platforms like JIRA is a plus. In addition, it is good to see as a customer we have been listened to as our feature requests are being implemented on a frequent basis.
What do you dislike about the product?
This might be a symptom of any monitoring or observability platform, but the more monitoring you put in place, the more maintenance overhead is required to keep the noise to a minimum. However Monte Carlo are continuously developing features to mitigate this, for example, the misconfigured monitors daily digest feature to help the team keep on top of stale or incorrect monitors.
What problems is the product solving and how is that benefiting you?
Monte Carlo allows us to see direct data quality issues but also provides visibility on signals that might indicate more significant errors in our data pipelines. This allows us to reduce the number of issues reaching our data consumers and reduces the overal impact of errors in our reporting use cases. It also takes away a lot of the development effort required to set up alerts and notifications to alert our engineering teams of incidents. The incident management process is streamlined as a result of using Monte Carlo.
Critical tool to have, with occasional quirks
What do you like best about the product?
Monte Carlo is best at providing critical data content testing and pattern recognition out of the box, wihch would be extremely time intensive to setup manually. Additionally, the ability to trace lineage on events, and identify affected dashboards and content is great. Customer support is also great, and are responsive to needs.
What do you dislike about the product?
Sometimes incidents can be erroneous, and nailing down the exact criteria to prevent "alert fatigue" can be tricky.
What problems is the product solving and how is that benefiting you?
The biggest piece is monitoring data content and patterns. You can quickly determine if an average for a fct metric is way out of whack from historical averages, if new values in enum are added, or schemas change. Many of these are out of the box, and take no effort. This is critical in a small team, where manpower is the limiting factor.
Best data observability platfotm
What do you like best about the product?
Useful and informative reports, simple interface, customizable monitoring of each source and database, real-time monitoring of data lineage
What do you dislike about the product?
For now everything is good, this tool is really helpful for me
What problems is the product solving and how is that benefiting you?
It helps us see data lineage to understand if there is an issue downstream or upstream.
We need less data analysts to actively monitor the reports
We need less data analysts to actively monitor the reports
Provides centralized data observability features and has an easy-to-use user interface.
What is our primary use case?
What is most valuable?
The product allows us to segment data into domains and categories. It makes organizing work easier based on its relevance to specific projects and teams. Additionally, the orchestration layer within the tool plays a vital role in streamlining data processes.
What needs improvement?
For anomaly detection, the product provides only the last three weeks of data, while some competitors can analyze a more extended data history. This feature needs improvement. Its price could be a bit competitive compared to competitors offering similar services.
For how long have I used the solution?
We have been using Monte Carlo for three years.
What do I think about the scalability of the solution?
The product is scalable. However, they could improve the pricing and provide more cost-effective ways of scaling.
How are customer service and support?
They offer efficient customer service. They provide essential documents, feedback, and solutions. They set up a meeting with us whenever we require more information.
How would you rate customer service and support?
Positive
How was the initial setup?
The product's initial setup is in a daily improvement stage, deploying new plugins for upstream and downstream resources. It takes 25 minutes to complete. The process involves integrating with third-party services for Single Sign-On (SSO). It requires only one executive for maintenance as it has easy-to-use navigation and user interface.
What's my experience with pricing, setup cost, and licensing?
The product has moderate pricing.
What other advice do I have?
The product has centralized nodes and is a pioneer in the data observability domain. It has helped a lot in investigating system issues. It also saves a lot of time in identifying issues by improving data traffic.
I rate Monte Carlo a nine out of ten.
Monte Carlo is complex
What do you like best about the product?
MC has been a great product for us for a while, allowing us to make complex SQL queries and automated queries providing alerts we need. These alerts help us know when things break and where along the pipeline we need to implement a fix.
What do you dislike about the product?
We have recently debated the benefits of Monte Carlo because there are other products we use that can cover the same category. These can include automated queries in Airflow or Hightouch. Also, MC's UI can be a bit too complex to navigate.
What problems is the product solving and how is that benefiting you?
MC is helping us keep track of our data and alerting us when things look off. We often use the anomaly prediction, allowing us to catch when a certain metric looks off. These changes are sometimes nonissues, but its nice to know about them anyway. We also use MC to get us lists of bot traffic from our site, which we can then exclude from our data.
QA Engineer`s experience with Monte Carlo
What do you like best about the product?
It's easy to manage dq checks;
the ability to integrate with github;
automatic search for anomalies (ex: a Large addition/ deletion of rows);
the ability to see a detailed description of the table;
the ability to view upstreams and downstreams;
technical support is very good and responds quickly.
the ability to integrate with github;
automatic search for anomalies (ex: a Large addition/ deletion of rows);
the ability to see a detailed description of the table;
the ability to view upstreams and downstreams;
technical support is very good and responds quickly.
What do you dislike about the product?
Too much visual content on pages;
table & field lineage slow down summary page;
features critical for our team are developing slow;
there are problems with integration with redshift.
table & field lineage slow down summary page;
features critical for our team are developing slow;
there are problems with integration with redshift.
What problems is the product solving and how is that benefiting you?
Data monitoring was not configured, due to which customers have being found the problems before us.
Now with MC we find a problems in a timely manner and quickly fix them
Now with MC we find a problems in a timely manner and quickly fix them
Product review for MonteCarlo
What do you like best about the product?
- Support relationship: Very reactive & super clear
- Product roadmap: Continous improvements
- Product roadmap: Continous improvements
What do you dislike about the product?
For now there are nothing particular to dislike
What problems is the product solving and how is that benefiting you?
- Data quality, lineage
- Interaction with data incidents
- More pro-active in finding issues
- People are more productive as they are able to identify the problem quickly
- Interaction with data incidents
- More pro-active in finding issues
- People are more productive as they are able to identify the problem quickly
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