406 Monte Carlo Reviews
Our Team loves the out of the box monitors in Monte Carlo, they make time to value much shorter and allow the product to start adding value quickly while you work with the Monte Carlo team on more targeted monitoring capabilities. Really can't stress enough how responsive and helpful the support team is. Review collected by and hosted on G2.com.
We do see some issues with our monitors in Monte Carlo from time to time where we are using them in non-standard use cases, generally these show up as data not matching our expectations within the monitoring results but every time this has come up so far we have been able to get to the bottom of it with help from the support team. Review collected by and hosted on G2.com.
We've been using Monte Carlo for a couple of years now, and it's become an essential part of our data engineering toolkit. It delivered value almost immediately—helping us uncover data quality issues we didn't even know existed. Between the machine learning-driven anomaly detection, our custom domain-specific monitors, intuitive lineage and query history features, and excellent customer support, Monte Carlo plays a vital role in helping us meet our data quality goals. Review collected by and hosted on G2.com.
Monte Carlo moves quickly, and while we appreciate the pace of innovation, early on it sometimes felt like there was too much change all at once. Additionally, the platform has a wide range of features—which is a strength—but it can occasionally be challenging to remember where to find some of the more nuanced settings or controls. Review collected by and hosted on G2.com.
Comprehensive Monitoring: The automated monitors track data freshness, volume and schema changes. Additional monitor can track quality across multiple sources with some manual setup.
Fast Issue Detection: Speeds up incident discovery and resolution, helping reduce the time bad data goes undetected.
Scalability: Works well across large, complex data ecosystems with minimal performance impact.
Integration-Friendly: Supports a wide range of data warehouses, lakes, pipelines, and BI tools.
Support: Support team is professional and provides answers in a very timely manner. Product team is very cooperative and open to ideas/improvments Review collected by and hosted on G2.com.
Cost: Pricing can be high, pricing policy sometimes changes.
Ramp-up Time: While setup is generally straightforward, configuring monitors effectively for all business-critical datasets can still take effort.
False Positives: Especially early on, teams might experience a higher volume of alerts that need tuning to avoid noise and fatigue. Review collected by and hosted on G2.com.
Out-of-the-box machine learning monitors that check for freshness, volume, and schema changes are awesome! Review collected by and hosted on G2.com.
To be very honest, it's the best technology I have used for data observability and data quality checks at scale. But if I have to point out one thing - it would be those additional features that come with the integrations. For eg - MC tries to show traces of our airflow integration at multiple places but at some places, I don't find it accurate. Same thing goes with dbt integration. Review collected by and hosted on G2.com.
I’ll say few positive things:
1. The UI is very good, easily can create custom alerts and to investigate the data
2. The ability to connect MC with many alerts pipes such as Slack, mail
3. The sensitivity feature that allow us as data users to control when MC alerts will take action Review collected by and hosted on G2.com.
1. Sometimes the default alerts sensitivity is too high, and then I get spam alerts (for example added 10K rows, usually 10.5K rows). I’ll prefer that the default will be less sensitive
2. The custom alerts title to Slack requires 1 row, which requires aggregation of the query into a single row. It would be more convenient if MC would already take the column and collapse it into a single row automatically w\o using SQL function Review collected by and hosted on G2.com.
Monte Carlo handles the complex data monitoring tasks and allows us to utilize our own SQL and business rules. We monitor our data by multiple segments and Monte Carlo makes that easy, alerting us when things go sideways. The Monte Carlo team also listens to us when we have ideas for improving the product (and our monitoring), and is constantly enhancing their product to meet customer needs. Review collected by and hosted on G2.com.
It's hard to pick something I really dislike about Monte Carlo. We tend to use the anomalous detection more than hard/fast rules, and there are situations where we'd like a little more control over the acceptable ranges. Review collected by and hosted on G2.com.
Monte Carlo gives us proactive visibility into data issues before they impact downstream stakeholders. The automated monitoring across tables, columns, and freshness saves our team countless hours we used to spend manually checking data pipelines. The integration with tools like Slack and dbt makes it seamless to stay on top of data health without leaving our workflow Review collected by and hosted on G2.com.
While Monte Carlo is powerful, the UI can sometimes feel cluttered when navigating large numbers of monitors or incidents. Additionally, the alerting can occasionally be noisy until it’s fully tuned for our environment. More granular control over alert thresholds and grouping would make the experience even better Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
I was really impressed with how easy this product is to use. Right out of the box, setup was quick and straightforward with clear instructions. The interface is intuitive, and I didn’t need to spend time figuring out how it works—it just made sense. Even for someone who isn’t very tech-savvy, this product makes daily tasks simple and efficient. It’s clear that a lot of thought went into the user experience. Overall, if you’re looking for something that’s hassle-free and beginner-friendly, this is a great choice. Review collected by and hosted on G2.com.
Monte Carlo simulations often require a large number of iterations to produce accurate results, which can be very resource- and time-consuming, especially for complex models. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
