Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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Has many features for data observability and it evolves their features
What do you like best about the product?
We use Monte Carlo to monitor our entire organisation's data warehouse. Its ML-based thresholds make it extremely useful for us.
This allows us to plug in our tables and identify anomalies, while also enabling us to tune the models per alert.
This allows us to plug in our tables and identify anomalies, while also enabling us to tune the models per alert.
What do you dislike about the product?
In our organization, we have multiple tables with nested values. We are not able to plug these columns.
What problems is the product solving and how is that benefiting you?
Behavioral data observability.
Great Data Observability Tool
What do you like best about the product?
The ability to view the model lineage, tests, and alerts within a single application is the most valuable asset for any data team. We have been using MC extensively and would not be able to function without it.
What do you dislike about the product?
Too many alerts..Wish it was smarter in aggregating the alerts
What problems is the product solving and how is that benefiting you?
Monte Carlo is helping us serve better data to our product teams and the business by enabling us to see real-time data quality issues.
It has good features but not anything espetacular
What do you like best about the product?
Good features, easy integrations with Slack/PagerDuty/Jira/etc.
The ui is good, the design also looks good.
The ui is good, the design also looks good.
What do you dislike about the product?
There are other ways we can use to to receive alerts and I would say that you need more stuff to differentiate yourself.
I dislike the assets search but the biggest issue for me is that the jobs visualisations are terrible. If a failling happens fast, the bar will be so small I cant even click on that. Also, would be much better if I could filtered based on error instead of looking to all the 1000s models we have hourly/daily.
I dislike the assets search but the biggest issue for me is that the jobs visualisations are terrible. If a failling happens fast, the bar will be so small I cant even click on that. Also, would be much better if I could filtered based on error instead of looking to all the 1000s models we have hourly/daily.
What problems is the product solving and how is that benefiting you?
receiving alerts in slack, integration with other softwares and a visual presentation of our dbt logs so its easier to reference the erros in other places
Detailed but not the easiest UI
What do you like best about the product?
Detailed information about every data asset
What do you dislike about the product?
The UI is hard to grasp at first. It took me some time to understand when to find stuff.
What problems is the product solving and how is that benefiting you?
We use Monte Carlo to monitor our Data assets and finds potential issues.
Amaizing tool
What do you like best about the product?
I do like how easy is to implement Monitor as a Code with MC.
What do you dislike about the product?
There's nothing specifically that I don't like, but some features that could be nice to have are some templates with the most common checks already set.
What problems is the product solving and how is that benefiting you?
I don't need to wait the analyts to report a strange number in the dashboards, now I get notified every time we have something strange in the applications that were reflected in our data lake.
A valuable observability platform with great support.
What do you like best about the product?
Easy to use with an intuitive interface
Fast and helpful support team
Strong data quality checks (volume, freshness, schema changes, ...)
Clear data lineage across pipelines and assets
Simple and reliable monitoring & alerting setup
Fast and helpful support team
Strong data quality checks (volume, freshness, schema changes, ...)
Clear data lineage across pipelines and assets
Simple and reliable monitoring & alerting setup
What do you dislike about the product?
Field-level lineage can be confusing and lacks clear tracking
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us catch data issues early by monitoring volume, freshness, and schema changes. It reduces time spent debugging and gives us confidence in our data pipelines with clear alerts and lineage tracking.
Comprehensive Data Quality with Easy Setup
What do you like best about the product?
I really appreciate Monte Carlo for its easy-to-navigate UI and the high level of automation it operates with. The best part is that everything is configurable via code. The coverage is amazing, and even though we haven't used it extensively, the data lineage tracking is sometimes very useful and well built. The initial setup was extremely easy, which was a big plus.
What do you dislike about the product?
Cost is a big tradeoff, for a tool that wants us to have full 100% coverage for the best possible setup, for larger orgs it's quite expensive due to the pricing structure. Google Bigquery has wildcard sharded tables which when added to be monitored via Monte Carlo acts as a single table, this means when this sharded table has a partition expiry it's treated as a row deletion unfortunately rather than a detected partition expiry. The out of the box ML powered training of the monitors could be refined a little. Sometimes it takes too many iterations to let Monte Carlo know what is normal and what isn't. It's worse when it's the opposite.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps with observability and data quality, offering proactive monitoring to catch anomalies early and fighting data downtime. It improves trust by providing visibility into systems and sometimes identifies bugs in production pipelines.
Great data monitoring and observability tool
What do you like best about the product?
1. Comprehensive Monitoring for Data Integrity
Monte Carlo excels in offering detailed monitoring options to help us ensure complex data models remain updated and maintain high integrity. The platform allows our team to set up customized monitors to track data quality metrics, such as freshness, completeness, and accuracy. This level of granularity is invaluable for organizations managing intricate data pipelines, as it helps identify anomalies before they impact downstream processes. The ability to configure monitors tailored to specific datasets ensures robust oversight and minimizes the risk of data issues going unnoticed.
2. Flexible and Customizable Alerting
The alerting system in Monte Carlo is a standout feature, providing us with control over how and where they receive notifications. When data issues arise, the platform can send alerts through Slack, which we use daily. This flexibility ensures that our team members are promptly informed, enabling quick resolution of issues. The ability to customize alert thresholds and destinations enhances operational efficiency and aligns with diverse team workflows.
3. Seamless Integration and Data Lineage
Monte Carlo integrates effectively with popular data tools like dbt and Tableau, enabling us to visualize table, column, and dashboard lineage and inform our stakeholders accordingly. This feature is particularly useful for understanding data dependencies and tracing the flow of data across systems. The clear visibility into lineage helps our teams debug issues, assess the impact of changes, and maintain trust in our data. By connecting with existing data stacks, Monte Carlo enhances its utility as a centralized observability hub.
Monte Carlo excels in offering detailed monitoring options to help us ensure complex data models remain updated and maintain high integrity. The platform allows our team to set up customized monitors to track data quality metrics, such as freshness, completeness, and accuracy. This level of granularity is invaluable for organizations managing intricate data pipelines, as it helps identify anomalies before they impact downstream processes. The ability to configure monitors tailored to specific datasets ensures robust oversight and minimizes the risk of data issues going unnoticed.
2. Flexible and Customizable Alerting
The alerting system in Monte Carlo is a standout feature, providing us with control over how and where they receive notifications. When data issues arise, the platform can send alerts through Slack, which we use daily. This flexibility ensures that our team members are promptly informed, enabling quick resolution of issues. The ability to customize alert thresholds and destinations enhances operational efficiency and aligns with diverse team workflows.
3. Seamless Integration and Data Lineage
Monte Carlo integrates effectively with popular data tools like dbt and Tableau, enabling us to visualize table, column, and dashboard lineage and inform our stakeholders accordingly. This feature is particularly useful for understanding data dependencies and tracing the flow of data across systems. The clear visibility into lineage helps our teams debug issues, assess the impact of changes, and maintain trust in our data. By connecting with existing data stacks, Monte Carlo enhances its utility as a centralized observability hub.
What do you dislike about the product?
Enhanced Documentation and Examples for Monitors as Code
While Monte Carlo supports "monitors as code" for implementing custom monitors, the documentation and examples provided could be more comprehensive. We sometimes face challenges understanding how to implement certain more complex / custom monitors due to limited or unclear guidance. Expanding the documentation with detailed tutorials, real-world examples, and best practices would make it easier for teams to leverage this functionality. Clearer explanations of syntax and use cases would reduce the learning curve and improve adoption.
While Monte Carlo supports "monitors as code" for implementing custom monitors, the documentation and examples provided could be more comprehensive. We sometimes face challenges understanding how to implement certain more complex / custom monitors due to limited or unclear guidance. Expanding the documentation with detailed tutorials, real-world examples, and best practices would make it easier for teams to leverage this functionality. Clearer explanations of syntax and use cases would reduce the learning curve and improve adoption.
What problems is the product solving and how is that benefiting you?
We build datamarts for our complex business supporting over 15 markets. As such we need data to be timely and highly trusted. Monte Carlo helps us with observability and allowing us to customise the monitoring and alerting in a way that works for us (slack, dbt, tableau integrations)
Good for monitoring and table lineage
What do you like best about the product?
Table lineage, alerts via email, alert set-up is pretty straightforward, table data monitoring is very good
What do you dislike about the product?
Alerts via slack/teams could be a bit nicer. Table or field lineage could be more human friendly: in example if I asked how did this column got calculated, I wish AI would summarise me in human language how this field turn out to be the way it is.
What problems is the product solving and how is that benefiting you?
Any missing data is usually spotted, any custom alerting is well integrated, able to create different alerts for variety of stakeholders
Using for alerts
What do you like best about the product?
freshness alerts are useful to detect failing tasks
What do you dislike about the product?
requires easier management of objects to track
What problems is the product solving and how is that benefiting you?
data freshness alerts
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