Monte Carlo Reviews & Product Details

Monte Carlo was very helpful in aligning and working with stakeholders to help show what kinds of issues a Data Platform team could monitor effectively, and where we would need more business involvement.

While it can be easy for engineers to scoff at queries to the information schema, the overall tool was very helpful in providing an archive of experiences and helped us build runbooks around actions taken by both senior and junior engineers. Review collected by and hosted on G2.com.

The in built query editor and AI autocomplete tools can be a bit frustrating to work with - our folks will typically just copy from native sql tools.

I can't say this is a dislike, but rather an outcome that could have gone wrong.

The ease of set up can quickly create a deluge of new alerts - especially out of the box anomaly detection - where not everyone understands what is running or how to respond, or if stakeholders need to worry. We were fortunate to have an appropriate amount of time running in production with the teams that will use it, before bringing our few stakeholders together, and were able to determine (rather ad hoc) what was meaningful and what wasn't. Review collected by and hosted on G2.com.

Monte Carlo brings a high degree of governance, change management, and automation to this product sphere that make it a great fit for compliance control automation. Our organization has taken prior manual compliance testing scenarios and the concept of controls generally into Monte Carlo. Integration with tools like Slack enable smooth alerting, response, and remediation. Monte Carlo also adds value through more proactive insights on anomalies in data tables that help us get ahead of emerging incidents. Review collected by and hosted on G2.com.

Excited to see Monte Carlo increase it's accuracy and effectivness in proactively surfacing potential anomalies based on patterns in data tables. Specifically getting more advanced at detecting nuanced seasonal changes or patterns related to metadata in other tables in more dynamic ways. Review collected by and hosted on G2.com.

One of the things I really appreciate about Monte Carlo is its automated, out-of-the-box monitors powered by anomaly detection, which learn from our data patterns and alert us to irregularities. It has quickly become an indispensable tool for uncovering unknown data quality issues in our daily operations. Review collected by and hosted on G2.com.

Monte Carlo is less effective for infrequently updated data, such as bi-weekly, monthly, or quarterly datasets, as the out-of-the-box monitors are not designed to support these use-cases. While custom monitors can address this, they sacrifice scalability, reducing the tool's overall usability for these use cases. Review collected by and hosted on G2.com.

When it comes to Data Observability, it's the best tool in the market. It's possible to check accurately the freshness, history of changes in tables, details... Fundamental for Data Engineering and Data Governance Teams. I use it on a daily basis and I recommend the tool. As well, it is possible to integrate Monte Carlo with modern platforms such as Snowflake or PowerBI, what it makes it even better. Review collected by and hosted on G2.com.

The tool has great potential, but its current approach to user permissions is holding it back. Right now, managing roles feels like navigating a maze through "audiences", which makes it incredibly frustrating to control who sees what. Review collected by and hosted on G2.com.

The montecarlo is giving me a lot of posibilities in terms of data quality. I can setup the notifications, create a groups of people and send them a notification if something failes Review collected by and hosted on G2.com.

As the user of google chat I'm realy anoyed that I need to use emails. For me the best way for alerts will be a direct message to a google chat group. The best way will be use the webhooks that google is providing Review collected by and hosted on G2.com.

The tool provides early detection of data quality issues & data lineage from source to target, giving visibility of dependencies and the impact it will have on downstreams. It also improves data reliability and reduces the time and effort needed for data debugging Review collected by and hosted on G2.com.

there are no downsides. It would be better if we can improve on the speed as its bit on a slower side. Review collected by and hosted on G2.com.

Monte Carlo is ridiculously easy to use. Implementing monitoring in the datasets is a few mouse clicks and the machine learning algorithm picks up the patterns in the datasets. The default monitoring (row count, freshness monitor, query logs, schema changes) are exactly what I need for most of my datasets. It's so easy to learn to use! I've managed to implement custom sql monitoring and tests without too much consulting of the manual as MC is really intuitive. I use it in all of my datasets for daily monitoring.

The slack integration and setting up "Audiences" for any alerts is quick and easy, and I love that you can send test alerts to make sure things are working Review collected by and hosted on G2.com.

I'd like to be able to get alerts that output variables dependent on the issue, but that's not something I've managed so far. I haven't reached out for help though, so it could be a me issue! Review collected by and hosted on G2.com.

I think Monte Carlo is a great way to monitor data issues and I love the "built-in" freshness/volume anomaly monitors on any tables added to Monte Carlo. Review collected by and hosted on G2.com.

We are using the Monte Carlo product to monitor our BigQuery tables. I have chatted with Monte Carlo support about this before and put in a ticket; but it would be great if we were able to set variables within Monte Carlo monitors (we wanted to use a list within the monitor in order to take advantage of partitioning in BigQuery, as BigQuery does not support dynamic partitioning and thus a CTE would not use partitioning correctly).

Scenario:

Using a list within a Monte Carlo monitor results in failure. The MC monitor simply takes the first output written in the monitor (the result of setting the list) and considers that as the monitor. The rest of the code in the monitor (after the list is set) is not considered.

You can see the ticket or contact me for additional details/explanation.

Additionally, I think it would be useful if there were more automated monitors (for example, you could set up an automated monitor so that for ANY anomalous value in the table, the monitor is triggered). Review collected by and hosted on G2.com.

The asset tab / data catalog is really good! I love how I can directly look at how frequent the table is updated and how many new rows every update have. I also love the fact that we can trace down the upstream and downstream queries/tables. This is really useful cause it allows me examine the definition of each column without having to figuring who owns the query and where I can find the definition. Review collected by and hosted on G2.com.

The experience is good overall. One thing I would note that I use the custom SQL a lot. Part of the reason is the smart alert sometimes can output unpredictable alert where it is more predictable with a threshold in custom SQL. Also, it is unclear to me if marking the alerts as "expected/no action needed/etc." feeds into the algo and makes the alert better. Review collected by and hosted on G2.com.

Monte Carlo is very straightforward to use. The UI is very intuitive, and it is very easy to set up and quickly get started monitoring your data.

There are many features and monitors, and the product is constnatly evolving and improving.

The customer support team are very responsive, and feedback is genuinely listened to and acted upon.

Monte Carlo has many different integrations, and so is easy to use with most mainstream data tools. We have found that the "lineage" feature that Monte Carlo provides is actually more useful than that provided by Databricks Unity Catalog, because we can see the data flow end-to-end across all our different platforms (not just databricks). Review collected by and hosted on G2.com.

Monte Carlo is quite a young product, which means it is constantly evolving.

This is great, because it means that features are being added all the time and the product is improving. However, it does mean that the interface often changes and the documentation is out of date.

It would be great if the monitors-as-code feature had some more support. At the moment, monitors-as-code is limited which means that the default is to create monitors through the UI, but this means there is limited visibility on which monitors have been created for which datasets, and minimal access crontrol. Review collected by and hosted on G2.com.