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Monte Carlo is a Game Changer
What do you like best about the product?
Monte Carlo is a tool that is easy to implement, use, and drive value through for users across our organization. In our case, we started seeing value during the first week of our proof of concept (POC) period and have continued to generate more value since!
A big reason for this is that Monte Carlo not only helps identify a data anomaly faster than we had ben able to before, but also quickly enables users to understand the context and impact of the event. Lineage, Impact Analysis, Correlation Analysis, and integrations with various tools helps you put the alert into context and also resolve the issue much faster than an out of context alert.
Outside of the abilities of the core product, the support team is top notch and actively takes feedback to enhance the product. The team is truly customer first. We have never had a customer support request fall off the radar and they are always proactive in making sure we find the best solution for our needs.
Lastly, Monte Carlo is a tool designed for scale and the product is centered around making it easy for an organization to standup up a data quality program (with support activities) all through the tool.
A big reason for this is that Monte Carlo not only helps identify a data anomaly faster than we had ben able to before, but also quickly enables users to understand the context and impact of the event. Lineage, Impact Analysis, Correlation Analysis, and integrations with various tools helps you put the alert into context and also resolve the issue much faster than an out of context alert.
Outside of the abilities of the core product, the support team is top notch and actively takes feedback to enhance the product. The team is truly customer first. We have never had a customer support request fall off the radar and they are always proactive in making sure we find the best solution for our needs.
Lastly, Monte Carlo is a tool designed for scale and the product is centered around making it easy for an organization to standup up a data quality program (with support activities) all through the tool.
What do you dislike about the product?
I think there are still some enhancements actively being made to help users more easily administer the tool, but overall, there are not any big dislikes about Monte Carlo.
What problems is the product solving and how is that benefiting you?
Monte Carlo has given us end to end observaiblity of key data pipelines that power the core of our business. Data trust is a key factor in any analysis and Monte Carlo has increasingly enabled us to trust our data and quickly take action when things begin to look off.
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Montecarlo review
What do you like best about the product?
- easy to integrate with current ecosystem ( data warehouse, dasboarding tool, SQL applications)
-UI are modern and userfriendly
-ML capabilties can identify a good porton of data issues
-UI are modern and userfriendly
-ML capabilties can identify a good porton of data issues
What do you dislike about the product?
we had some points in the past months but the team is addressing them rapidly
What problems is the product solving and how is that benefiting you?
-Issues on data pipeline
-Proactive data monitoring
-Proactive data monitoring
The ultimate guard for data issues
What do you like best about the product?
Good response time from their support team.
The support team really tries to understand our use cases.
Automatic monitors have been super useful for data engineering purposes.
Custom SQL rules look promising for anomaly detection.
dbt integration has been very useful for the analytics engineering team.
We appreciate the possibility to test beta features, like the Performance tab, which has been very helpful to us in a couple of projects.
We use it daily to review incidents and document.
The support team really tries to understand our use cases.
Automatic monitors have been super useful for data engineering purposes.
Custom SQL rules look promising for anomaly detection.
dbt integration has been very useful for the analytics engineering team.
We appreciate the possibility to test beta features, like the Performance tab, which has been very helpful to us in a couple of projects.
We use it daily to review incidents and document.
What do you dislike about the product?
Our BI tool, Metabase, is currently not supported.
What problems is the product solving and how is that benefiting you?
- Monitoring of data engineering pipelines. Monte Carlo has proven that logging tools such as Datadog are not enough for data engineers.
- Performance monitoring for analytics engineering.
- Performance monitoring for analytics engineering.
Game changer for the observability of a data platform
What do you like best about the product?
The out-of-the-box monitors based on ML.
What do you dislike about the product?
Still room of improvement in terms of UI and time to reflect in the interface changes applied to the configuration of assets and monitors.
What problems is the product solving and how is that benefiting you?
Applying data monitors at scale
Monte Carlo: an experience end-to-end for monitoring data quality
What do you like best about the product?
As a tool:
- it is straightforward to set up / implement with the out-of-the-box connectors
- great out-of-the-box monitors / visualization
- great incident management features
- the UI is very user-friendly
And a great customer support team!!
- it is straightforward to set up / implement with the out-of-the-box connectors
- great out-of-the-box monitors / visualization
- great incident management features
- the UI is very user-friendly
And a great customer support team!!
What do you dislike about the product?
- Cannot detect an incident before a code is pushing into production: only when it's running in production
- We tried to implement a connection between a SQL Server and Monte Carlo and the connection between Monte Carlo and this on-premise instance wasn't the easiest to manage by knowing that the features proposed by this connector are quite limited
- We tried to implement a connection between a SQL Server and Monte Carlo and the connection between Monte Carlo and this on-premise instance wasn't the easiest to manage by knowing that the features proposed by this connector are quite limited
What problems is the product solving and how is that benefiting you?
To detect incidents in real-time and to be pro-active in the incidents solving
We use MC to monitor a significant number of our tables.
What do you like best about the product?
Good connectivity between different tools e.g. slack, links go directly to GCP BQ etc
What do you dislike about the product?
We have many slack updates set up and the slack updates provide significantly less detail than the images produced on the MC incident page itself
What problems is the product solving and how is that benefiting you?
Allowing us to let our processes run automatically without having to frequently check our tables for data quality. Issues in our source data or processes are identified and the alert is sounded before these issues can reach the business
Monte Carlo Integration for GCP DWH
What do you like best about the product?
Monte Carlo helps us keep a close eye on our data warehouse. Examples include notifying us of major changes in volume or freshness, which helps us get ahead of potential issues, and table / column lineage, which lets us see the impact of any changes to dependent tables / reports. I'm sure there are other useful features which we haven't explored yet too.
What do you dislike about the product?
No major downsides. Would like more customisation on notifications as they can be quite verbose. Maybe a few more options around the Inisght reports would be useful too. And maybe a dark mode for the GUI...... (it's on my wishlist for GCP itself as well.....).
What problems is the product solving and how is that benefiting you?
Daily DWH changes and issues.
Impact assessment.
Old / unused fields and tables.
Impact assessment.
Old / unused fields and tables.
Easy to use, helps bring insight into areas we had no awareness.
What do you like best about the product?
Implementation was pretty simple and straightforward. Support helped upgrade our collector and respond to our questions quickly. The UI has been updated with many features over time, and we are recently enjoying the performance tab for long-running write and read queries.
I comb through the alerts and monitors weekly to understand the patterns and use cases better. We have an on-call rotation that leans into incidents generated by Monte Carlo.
I comb through the alerts and monitors weekly to understand the patterns and use cases better. We have an on-call rotation that leans into incidents generated by Monte Carlo.
What do you dislike about the product?
The feedback loop for our dbt runs are not immediate. If we have an issue with a model that causes skips we don't have an easy way to understand this and action on it in Monte Carlo which stops us from leaning deeper into the incidents workflow.
The insights in the performance tab are great, but it's not easy to drill down and you can't export.
I'd love to see top level if one table has an issue here are every downstream reports impacted as a snapshot of sharing to users.
The insights in the performance tab are great, but it's not easy to drill down and you can't export.
I'd love to see top level if one table has an issue here are every downstream reports impacted as a snapshot of sharing to users.
What problems is the product solving and how is that benefiting you?
Montecarlo has been very helpful for identifying issues with the breadth of jobs we have running. If something isn't updating as expected or a change happens and volumes spike, it's helped us resolve incidents immediately that may have otherwise been unseen.
We have also been building out domain-specific reporting to understand better which areas need improvement
We have also been building out domain-specific reporting to understand better which areas need improvement
Use of Monte Carlo Data Quality and Observability in Data Management
What do you like best about the product?
Ease of use and breadth of market leading capabilties, such as data lineage checks. Strong customer support and features request process.
What do you dislike about the product?
There is nothing that comes immediately to mind.
What problems is the product solving and how is that benefiting you?
The Observability checks ensure that the data freshness and volume checks are ensuring the right data is availble at the right time. This is important to the busienss users of the data.
Great out-of-the-box solutions and easy to deploy using infrastructure as code
What do you like best about the product?
- Really easy to set up the out-of-the-box monitors
- Generally quick cutomer service and response times
- Easy to monitor multiple tables in one place
- API explorer enabled us to explore the API before making use of the SDK
- Integration with Opsgenie and Slack
- Generally quick cutomer service and response times
- Easy to monitor multiple tables in one place
- API explorer enabled us to explore the API before making use of the SDK
- Integration with Opsgenie and Slack
What do you dislike about the product?
- Using the SDK was a bit challenging and the documentation could be improved
- Setting up custom monitors was a bit hit and miss
- Would like there to be better integration with Google Cloud services
- Setting up custom monitors was a bit hit and miss
- Would like there to be better integration with Google Cloud services
What problems is the product solving and how is that benefiting you?
Using Monte Carlo to monitor tables freshness means that we don't have to create monitors ourselves.
Exploring using Monte Carlo for trying to monitor SLOs, would like a way to integrate this with Google Cloud Monitoring.
Exploring using Monte Carlo for trying to monitor SLOs, would like a way to integrate this with Google Cloud Monitoring.
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