Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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MC Experience
What do you like best about the product?
managing thresholds for freshness and volume monitoring
What do you dislike about the product?
improved communication of new features through customer success team so we can better inform internal staekholders
What problems is the product solving and how is that benefiting you?
Identifying volume and freshness anomolies, performance monitoring, custom/opt in monitors for specific DQ checks
Monte Carlo is an awesome toll for Data Quality and Observability
What do you like best about the product?
Its an great tool to define the DQ rules
The Dahsboards are very intutive and easy to track incidents
Freshness & volume Monitors are very helpful
its userfriendy UI makes it easy to use the tool
The seamless integration with other techstack
The Dahsboards are very intutive and easy to track incidents
Freshness & volume Monitors are very helpful
its userfriendy UI makes it easy to use the tool
The seamless integration with other techstack
What do you dislike about the product?
The time takes to trains the models is bit
What problems is the product solving and how is that benefiting you?
In Traditional Data Quality setup is very difficult to keep track to all DQ issues at once. Monte Carlo is helping to have all DQ & Data Observibility at one place with easy to use UI interface
mc is successful in catching anomalies in terms of freshness and volume except false positive alerts
What do you like best about the product?
Automated monitors can quickly detect the anomalies and announce it via slack alert.
What do you dislike about the product?
Let's say an alert is sent to our slack channel about a freshness anomaly for several tables. When the anomaly of one of these tables is solved, but other still remains having the issue, it sends a thread message regarding as the current late amounts of the tables, including the table which has no freshness anomly anymore. However, we expect to the see only the current problematic tables in the thread messages.
What problems is the product solving and how is that benefiting you?
it detects the abnormal size changes and the freshness anomalies of our tables. Additionaly, we are informed about the schema changes like a type change of a column in our table.
A good tool to have
What do you like best about the product?
It helps us to do anomaly detection pretty easily and discover a lot of errors we wouldn't otherwise.
What do you dislike about the product?
Sometimes one wishes to have more integrations to have end to end quality tests. It doesn't support crossed soures lineage (data sharing)
What problems is the product solving and how is that benefiting you?
* Knowing which assets have data quality issues
* Knowing where this quality issues come from
* Knowing where the data issue is being propagated
* Knowing where this quality issues come from
* Knowing where the data issue is being propagated
Alerts are a must
What do you like best about the product?
The "automatic" alerts MonteCarlo provides help us a lot to address data quality issues.
What do you dislike about the product?
No support for Redshift data sharing and databases built on top of datashares as of today.
What problems is the product solving and how is that benefiting you?
We can be proactive when issues arise instead of being reactive and we can alert business before they find the problems, increasing trust in our team.
Monte Carlo is a game changer for data observability in the modern data stack
What do you like best about the product?
The automated data monitoring and anomoly detection as added more value than even a small team of humans ever could for us. The automated monitoring has become a crucial tool for us in understanding our data health, lineage, and reliability, ensuring high data integrity across the ecosystem. Additionally, Monte Carlo excels in proactive incident management, with robust alerting mechanisms that have enabled us to swiftly address and resolve issues, thereby minimizing operational impact. The ease of implementation with data sources and integration with communication tools (Slack!), provides us with actual real-time insights in not only our pipeline health, but also resolution of issues.
What do you dislike about the product?
The user-interface can be a little confusing in some of the more advanced features of the app, but the user support team is always extremely responsive to questions.
What problems is the product solving and how is that benefiting you?
The automated anomoly detection and data monitoring provides a robust but generalized testing harness which means we don't need to worry that our pipeline might fail because we weren't aware of a issue brewing due to missed tests during model development. Monte Carlo also provides a very slick intergration with Slack which we use to manage incidents. We have dedicated channels setup for incident priority, and individual users are alerted on specific monitors. From there, users can apply or review status flags directly to an incident without needing to open the UI. This process alone has saved us an immense amount of time and resources.
Matured Data observability tool
What do you like best about the product?
Automated alerts based on ML prediction and ease of scalling
What do you dislike about the product?
UI still can be improvised, sometimes I feel lot of information are shown on UI and hard for user to navigate
What problems is the product solving and how is that benefiting you?
Currently it is helping us to detect pipeline downtime by raising data freshness issues
Good, convenient, fast product
What do you like best about the product?
Convenient flexible setup of monitors and alerts; custom SQL covers almost all needs. The Table Lineage section is very convenient and useful. Good support.
What do you dislike about the product?
Lack of timestamp parameterization for monitor launches.
Not very convenient display of Tableau workbooks in the Lineage and Assets - Report sections.
And it would be convenient to have the ability to import descriptions for table and fields from Redshift data warehouse
Not very convenient display of Tableau workbooks in the Lineage and Assets - Report sections.
And it would be convenient to have the ability to import descriptions for table and fields from Redshift data warehouse
What problems is the product solving and how is that benefiting you?
Fast and convenient setup of automatic monitoring and user notification
Broad feature set, does what it says on the tin
What do you like best about the product?
Reduces pipeline complexity to actionable insights. Broad set of integrations. Love the API.
What do you dislike about the product?
Keep working on the UI/UX - UI could be more sleek.
What problems is the product solving and how is that benefiting you?
Detecting and reporting silent failures in a timely manner
Anomaly detection you would never detect without Monte Carlo
What do you like best about the product?
The few configuration necessary to get started. Monte Carlo really is a smart tool working on his own, and I find this great.
What do you dislike about the product?
The lack of Monitor as Code capabilities. OPS Analytics aims to have all conf stored as code (Terraform)
The lack of the possibility to control monitored/observable assets easily.
The lack of the possibility to control monitored/observable assets easily.
What problems is the product solving and how is that benefiting you?
Detect anomalies before end-users find about them in their dashboard.
Save time on resolution using insights.
Save time on resolution using insights.
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