Monte Carlo Reviews & Product Details

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 Review collected by and hosted on G2.com.

There should be a way to save or create a set of investigation queries for common alerts. May it's the way we implemented MC but the alerts could be moree useful maybe like a git style, what has changed between the last successful query and the one that broke Review collected by and hosted on G2.com.

I think the alerts are really valuable. Also the summary of the monitors give you a good understanding of what is happening with your tables. I use a lot the table lineage feature when I'm looking at a new table, it is easy to understand the dependencies of it, and how important it is. Review collected by and hosted on G2.com.

When I see the summary page, I can only see the last 30 days of the monitors' history. It is helpful but I'd really like to have more history, so I can better understand the incidents and check when some trends started.

Also, I activated the field health monitors for some important tables; however, the it is sending a lot of false positives alerts, generating alert fatigue Review collected by and hosted on G2.com.

- The tool overall works great. Offered features are enough to cover wide spread of use-cases.

- The team is also great. We've always had very quick response to our support requests.

- Automation features are the best. We've set up almost everything using code, which makes resources easily manageable.

- They are very open for feature requests and deliver them relatively quickly. We requested monitoring support nested fields and structs, which was implemented within weeks. Review collected by and hosted on G2.com.

- We had some headache during Databricks integration, which was mostly caused by our platform not being Unity enabled yet. But with support, we've dealt with all the issues.

- There is a blocklist feature which works on schema level. It would be great if we could block table level access with that feature. Review collected by and hosted on G2.com.

My team has been using Monte Carlo for a few years now and we've come to rely on it for ensuring that our data is what we think it is, which has opened the door for us to use different architectures then we could have otherwise.

As I said in the title, it lets us encode and enforce the invariants we see in our systems without requiring endless handwritten batch tasks or overloading shared resources. Running these queries on our OLTP database would be hugely impractical, if it were even possible, and running them on our data warehouse manually would be a joke. But allowing MC to run them daily means that we can easily add as many as we want and know that, unless we're being alerted, our expectations for the data are accurate. Review collected by and hosted on G2.com.

Some aspects of the UI are a bit unclear. For example, when making new alerts, syntax errors are not communicated at all, and there's an inexplicably short timeout, which can make testing some larger queries impossible. Review collected by and hosted on G2.com.

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.

Monte Carlo Data provides an exceptional level of detail and accuracy in its simulations, allowing for robust risk assessment and decision-making. The user interface is intuitive, making it easy to set up and run complex models. Additionally, the ability to customize parameters and visualize outcomes through dynamic charts and graphs enhances the overall user experience. Review collected by and hosted on G2.com.

While the tool is powerful, it can be resource-intensive, requiring significant computational power for large datasets. Additionally, the initial learning curve can be steep for users unfamiliar with statistical modeling, and more comprehensive tutorials or user guides would be beneficial. 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.

I think it's awesome at finding issues in our system that we aren't able to capture with our own validation tools. Review collected by and hosted on G2.com.

It takes a lot of set-up and tuning. We've had to disable a lot of the out of the box monitors for specific assets because they were too noisy. Review collected by and hosted on G2.com.

Monte Carlo's out of the box monitors create a relatively easy way to set yourself up for some potential big wins. Alerting that your source volume shows a small dip below expectation can potentially uncover a big issue. Focusing on critical data first sets you up to avoid an overwhelming number of alerts as the product 'learns' your data. Review collected by and hosted on G2.com.

Monte Carlo is early in the process to support the integration of Data Observability and the supporting Data Pipelines' state. Review collected by and hosted on G2.com.