Monte Carlo Reviews & Product Details

The built-in machine learning monitors that track freshness, volume, and schema changes are fantastic. I really appreciate how these features work right out of the box. Review collected by and hosted on G2.com.

To be completely honest, this is the best tool I have used for data observability and large-scale data quality checks. However, if I had to mention one drawback, it would be the extra features that come with the integrations. For example, MC attempts to display traces from our Airflow integration in several areas, but I have noticed that the information is not always accurate in some places. I have observed a similar issue with the dbt integration as well. Review collected by and hosted on G2.com.

I've found field lineage to be far more useful than I originally imagined. The table importance scale is also very nice to see. It has allowed us to get ahead of data quality alerts before our stakeholders are even aware of anything wrong. I find it easy to navigate especially and track down the most important models. There is a feature that let's you know if a query has changed based on the number of characters in a query, which is really nice. Review collected by and hosted on G2.com.

I really wish there was a way to snooze the monitors and alerts in the same manner, as it can sometimes become overwhelming. Review collected by and hosted on G2.com.

Monte Carlo has allowed us to monitor our data pipelines with increased clarity. One of its standout features is its ability to catch errors before they reach production, significantly reducing downtime and ensuring data integrity.

This product also played a crucial role in supporting our new client-facing data product. Its robust error detection and comprehensive reporting capabilities enabled us to launch with confidence, knowing that our data was accurate and reliable. Review collected by and hosted on G2.com.

The learning curve for setting up monitors, and understanding the system, was steeper than expected. Combined with the large number of tables in our warehouse, it was a laborious implementation process. Some of these issues are unavoidable. In the future I'm curious if there's a more efficient way to set up monitors. For example, in our case we set up the exact same rules for multiple tables, with the only difference being the field name and some slight variations in the SQL. Review collected by and hosted on G2.com.

I like how Monte Carlo brings data quality insights to the people who can fix them, the users of the data sources. I also find the ML thresholds helpful because they let Monte Carlo handle the error alerts, so the data platform team doesn't have to create the error thresholds manually. The integration with Slack is another plus, as it offers a centralized place for alerts and makes it easy to send them to the right stakeholders. Monte Carlo is easy to use, even though I didn't handle the initial setup. Review collected by and hosted on G2.com.

I'm having challenges with integrating Monte Carlo with AI agents. It would be great if AI agents could interact more seamlessly with Monte Carlo. Review collected by and hosted on G2.com.

I like Monte Carlo for its AI features that automatically handle the creation of boundaries when you select a source to be monitored. The automatic monitoring of schema changes, metric changes, and freshness is also great. I appreciate its integration with Slack, enabling the creation of automated workflows and keeping everyone informed proactively. The AI feature and automatic monitoring save a lot of time by eliminating the need to manually think about boundaries or constantly check for schema changes. Setting up the system was very easy, as all systems were connected quickly through admin accounts, taking less than a day. Review collected by and hosted on G2.com.

The main thing I don't like about Monte Carlo is how you need to select tables. We're really careful about what tables and sources we want to monitor, and that takes quite a lot of time. It's not super easy to navigate and select or deselect tables from a schema. That could be improved in my opinion. Review collected by and hosted on G2.com.

The flexibility and getting timely and reliable alerts for Volume, Schema and Freshness is useful. Able to tune the model is great. Review collected by and hosted on G2.com.

Not dislike, but couple of things that can be better:

1) Dashboards can be better in providing more actionable insights like most frequently failing tables or top 5 failing tables, under which schema, failing for what reason, frequently failing monitors, etc

2) It would be great if any updates made on alerts in Monte Carlo can flow into ServiceNow incidents

3) Additional integrations with files would be great, like if a file has not arrived, etc.

4) If we can have the model tuned for alerts much sooner than 2 weeks would be a welcome move.

5) Conducting any workshops on a sandbox environment for teams would help engage more teammates to understand and gets on with Monte Carlo Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The cost has increased considerably meaning we've had to monitor less tables to stay in budget. Would like more customisation on notifications as they can be quite verbose and the ability to interact with the notifications in GChat like we used to have in Slack. Maybe a few more options around the Inisght reports would be useful too. Review collected by and hosted on G2.com.

I like that we don't have to write our own DQ rules from scratch and its organized in a user-friendly UI. The data quality dashboard is a very useful tool to show executives and prove the ROI for the software. Review collected by and hosted on G2.com.

It can be complicated and overwhelming to understand the process as a whole on what to monitor, when to alert and what priority to assign. The popularity score doesn't always match with what the business considers our most important data and using the key asset tag doesn't allow the granularity to adjust how important an asset is. The AI features could use some work as they often offer suggestions that are not entirely helpful. Review collected by and hosted on G2.com.

I like Monte Carlo's advanced feature in data observability, which comes with useful pre-defined tools like freshness and volume monitor. I also appreciate the ability to customize them with custom SQL. The freshness monitor helps us ensure we receive data from our upstream/source systems and our downstream data products are refreshed as expected. If not, we get alerted, allowing us to troubleshoot and perform fixes promptly. Setting up Monte Carlo was easy with the official documentation, using the Monitor-as-code method with YAML configurations, which is helpful for developers to maintain in a Git repository. Review collected by and hosted on G2.com.

I wish there was more customization with the Monte Carlo alerts to write our custom messages, so that when they are sent to stakeholders like data product owners or source system owners, they can get better context of the alert. Review collected by and hosted on G2.com.

The depth of the monitors is excellent. The out-of-the-box ML stuff is great and spots changes that would normally go completely under the radar. On top of that, we can set up our own custom monitors for very specific business rules we need to check. It's a great mix of automated detection and hands-on control. Review collected by and hosted on G2.com.

Since we want coverage across all our assets, the alerts we get can get pretty noisy. It feels like we're trading full coverage for a very busy channel. I think this could be improved by making the monitor configuration a bit more intuitive. It can be hard to figure out how to best set the tolerances to avoid false positives, and some in-line examples or better guides would be a huge help in reducing the noise. Review collected by and hosted on G2.com.