Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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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.
Reliable Data Observability with Good Support
What do you like best about the product?
Monte Carlo's automated anomaly detection and lineage tracking have been incredibly valuable for us. It proactively identifies data quality issues, which saves us significant time and resources. We've also found the platform to be intuitive, and it integrates seamlessly with our existing stack. Added bonus is that the support team is readily available to assist and is quite knowledgeable!
What do you dislike about the product?
While the platform offers incredibly valuable features, some of the more advanced configurations can be a little complex. Although the documentation is generally clear, I've found that locating specific information can sometimes take a bit of time, particularly when it comes to the monitors-as-code functionality. For example, I recently spent some time figuring out how to properly configure and utilise runtime_variables through monitors as code. In fact, I ended up relying more on reading and understanding the code itself, as the documentation didn't fully explain how the implementation worked.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us proactively identify and resolve data quality issues across our pipelines before they impact downstream consumers. It reduces the time spent on manual data checks and increases trust in our analytics by alerting us to schema changes, missing data, and anomalies in real time. As a result, our team is more confident in the accuracy of our reporting and can focus more on delivering insights rather than firefighting data issues.
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
Innovative tool for Data Quality Alerts
What do you like best about the product?
Monte Carlo is tool that has enhanced our data processing capabilities.
Monte Carlo is a user-friendly tool that provides comprehensive visibility into our data processing activities. It offers a clear picture of what is ACTUALLY happening with our data, enabling us to make informed decisions and optimize our processes effectively. One of the standout features of Monte Carlo is its ability to self-learn based on observations. This means that it adapts to our data, ensuring that we get the accurate and relevant insights.
The visualizations provided by Monte Carlo are easy to understand, making it simple for everyone on the team to grasp the data insights. We have the ability to configure customized monitors and alerts, tailoring the tool to our unique requirements and preferences.
Monte Carlo is a user-friendly tool that provides comprehensive visibility into our data processing activities. It offers a clear picture of what is ACTUALLY happening with our data, enabling us to make informed decisions and optimize our processes effectively. One of the standout features of Monte Carlo is its ability to self-learn based on observations. This means that it adapts to our data, ensuring that we get the accurate and relevant insights.
The visualizations provided by Monte Carlo are easy to understand, making it simple for everyone on the team to grasp the data insights. We have the ability to configure customized monitors and alerts, tailoring the tool to our unique requirements and preferences.
What do you dislike about the product?
Be prepared to be amazed about what is actually going on with data processing. The alerts can be overwhelming at first but can be customized as needed.
What problems is the product solving and how is that benefiting you?
Monte Carlo is used to monitor Data Processing and to detect issues requiring stewardship or source data issues. Integrating this into our workflows will enable faster data cleanup.
MC good for basic setups, but want to see more investment to handle advanced flows
What do you like best about the product?
- Easy to navigate UI
- Integrates well with other tools (e.g. PagerDuty)
- Automated monitoring and alerting
- Automated segmentation (so you can keep everything in 1 monitor if desired)
- Integrates well with other tools (e.g. PagerDuty)
- Automated monitoring and alerting
- Automated segmentation (so you can keep everything in 1 monitor if desired)
What do you dislike about the product?
- Anomaly detection needs improvement (e.g. wide bands, misaligned with periodic patterns, false positive alerts)
- More product development needed to handle advanced workflows (e.g. process metrics with different latencies)
- More product development needed to handle advanced workflows (e.g. process metrics with different latencies)
What problems is the product solving and how is that benefiting you?
Automated metric anomaly detection and alerting
Simple but powerful monitoring application
What do you like best about the product?
Strengths:
1. Simplicity in UI
2. Intelligent grouping of related alerts & assets
3. Performance metrics at a query level
4. Integration into Jira and other platforms
1. Simplicity in UI
2. Intelligent grouping of related alerts & assets
3. Performance metrics at a query level
4. Integration into Jira and other platforms
What do you dislike about the product?
Weaknesses:
1. Lack of visibility in certain calculations
2. No automatic updates on past alerts (for example a field for "time resolved" or "valid from")
3. Dbt lineage not consistently captured
1. Lack of visibility in certain calculations
2. No automatic updates on past alerts (for example a field for "time resolved" or "valid from")
3. Dbt lineage not consistently captured
What problems is the product solving and how is that benefiting you?
Primarily with tracking alerts and detecting anomalies. The main benefit is reducing incidents by capturing potential issues as early as possible.
Nice tool with easy api access for developers
What do you like best about the product?
Freshness, volume and validation checks are helpful for users. It has sufficient api's for developers to use Monitor as code. Building dashboards are helpful.
What do you dislike about the product?
Completeness checks need to be improved. Alerts with new message after creating incidents is not currently supported. Some of the search option for filters in the UI for assets doesn't work often
What problems is the product solving and how is that benefiting you?
Custom monitors are really helpful in finding issues. Freshness monitors detects for any failed data arrival or dag failures. Slack alerts are easy to configure and can be routed to actual team responsible for alerts
An Amazing data observability tool to add to your data ecosystem
What do you like best about the product?
Monte Carlo is incredible, providing instant value right from the start. We heavily rely on their out-of-the-box monitors, especially the frequency and volume monitors. These monitors have helped us catch numerous unexpected data anomalies that would have otherwise gone unnoticed or been discovered much later. Another great aspect of Monte Carlo is their customer service; they are highly accessible, and their response time is very quick, which helps us resolve issues faster. We have bi-weekly sessions with them where we discuss recent data mishaps and explore ways to improve. Another great aspect is that Monte Carlo continually evolves their product with new services and stays up-to-date with the latest data innovations.
What do you dislike about the product?
There isn't particularly anything that we dislike about Monte Carlo. However, I think it would be beneficial if they had a feature request page where customers could submit new feature ideas. Additionally, being able to see what new features other customers have requested could help us explore some unexplored areas of the product and utilise its full potential.
What problems is the product solving and how is that benefiting you?
Monte Carlo effectively addresses several key challenges for us. Their out-of-the-box monitors, particularly the frequency and volume monitors, are invaluable in detecting data anomalies early on. This proactive alerting has prevented potential issues, such as data bloating due to duplicates, which could have led to significant costs if left unchecked. Although alert fatigue can occur, the ability to exclude certain datasets from monitoring has helped mitigate this issue. Overall, Monte Carlo's solutions enhance our data management and operational efficiency
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