Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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Lead Engineer
What do you like best about the product?
It does what it says it does, straight out of the box, little to no configuration required from our side
What do you dislike about the product?
It's relatively new, and therefore changes happen frequently, 9/10 times this is good as it brings additional features, but it can mean having to relearn
What problems is the product solving and how is that benefiting you?
It's helping support our entire data platform that is used across the company
Easily set up a much needed monitoring system.
What do you like best about the product?
Monte Carlo has a very easy to use UI and out of the box alerts/monitors.
What do you dislike about the product?
It takes a very long time for the machine learning models to calibrate and start sending alerts.
What problems is the product solving and how is that benefiting you?
I needed a way to be alerted to any database problems before they affected any stakeholders.
Great product!
What do you like best about the product?
Easy to use. Integrated with Slack and SQL for custom rules. ML is pretty good at detecting anomolies
What do you dislike about the product?
I was going to write "Nothing!" in this box, but the minimum character requirement is making me write nothing with many more words :|
What problems is the product solving and how is that benefiting you?
We need to proactively identify changes in our datasets, as well as have custom alerting based on table outputs that alerts us in Slack
Monte Carlo - High Value tool for Alerting and Monitoring of Data Platforms!
What do you like best about the product?
I recently started using the data monitoring tool Monte Carlo, and I am incredibly impressed with its capabilities. Since launching this tool at Cerebral, we have had a ~80% reduction in stakeholder-initiated downtime alerts. This has saved my on-call data engineering teams a lot of time and effort in identifying and addressing problems before they become significant issues, dramatically increasing trust in our data ecosystem (which is truly invaluable).
My team leverages the Monte Carlo slack alerter, which is a nice workflow for my engineering team. The Monte Carlo user interface is user-friendly, enabling it direct to set up and configure monitoring for my various data sources. The tool also offers a wide range of customization options, allowing my team to fine-tune our monitoring to fit our specific needs with GitHub version-controlled SQL.
Overall, I highly recommend Monte Carlo to any Series A company or beyond needing a reliable and efficient data monitoring tool beyond the use of Datadog or Cloudwatch in the application engineering ecosystem. Monte Carlo has proven to be an invaluable asset in managing and maintaining the integrity of our data.
My team leverages the Monte Carlo slack alerter, which is a nice workflow for my engineering team. The Monte Carlo user interface is user-friendly, enabling it direct to set up and configure monitoring for my various data sources. The tool also offers a wide range of customization options, allowing my team to fine-tune our monitoring to fit our specific needs with GitHub version-controlled SQL.
Overall, I highly recommend Monte Carlo to any Series A company or beyond needing a reliable and efficient data monitoring tool beyond the use of Datadog or Cloudwatch in the application engineering ecosystem. Monte Carlo has proven to be an invaluable asset in managing and maintaining the integrity of our data.
What do you dislike about the product?
One of the largest issues with Monte Carlo, is it's limited ability to integrate into other data monitoring tools in our data stack. For my team this includes a lack of direct integrations with DataDog or Pagerduty. This could limit its usefulness for some users who rely on a wide variety of data sources and need a monitoring solution that can easily integrate with them.
Complexity: Another potential shortcoming is that Monte Carlo may have a steeper learning curve for some users, even highly skilled MLEs or Data Scientists. While the tool offers a wide range of customization options which can be a plus for advanced users, it may be a little bit harder to understand and use for a beginner user.
Complexity: Another potential shortcoming is that Monte Carlo may have a steeper learning curve for some users, even highly skilled MLEs or Data Scientists. While the tool offers a wide range of customization options which can be a plus for advanced users, it may be a little bit harder to understand and use for a beginner user.
What problems is the product solving and how is that benefiting you?
Since launching Monte Carlo l at Cerebral, we have had a ~80% reduction in stakeholder-initiated downtime alerts. This has saved my on-call data engineering teams a lot of time and effort in identifying and addressing problems before they become significant issues, dramatically increasing trust in our data ecosystem (which is truly invaluable).
Proactive Data Quality Monitoring through Data Observability
What do you like best about the product?
Monte Carlo applies Machine Learning and Artificial Intelligence (ML/AI) to detect potential data pipeline failures and defects that might lead to significant data downtime, lack of customer satisfaction, and loss of revenue. Monte Carlo's automated and custom monitoring tools provide rich data monitoring insights along these dimensions: Freshness, Volume, Schema Changes, Distribution, and Lineage. With the automated, out-of-the-box monitors, you get Volume, Freshness, and Schema Changes alerts and notifications on your critical data assets. There is a graphical downstream and upstream lineage capability. With the Field Health custom monitor, you can create a check to detect anomalies caused by a deviation from the expected data distribution pattern.
What do you dislike about the product?
I do not have any significant criticism of the product, but I will like to see more integration between Monte Carlo and leading data catalog and metadata management applications. Data glossary and metadata tools provide a window into the world of business stakeholders and what they consider essential, i.e., critical. Therefore, a situation whereby we can present data monitoring insight to business users within the data glossary is, I believe, of paramount importance to the overall improvement of enterprise data hygiene.
What problems is the product solving and how is that benefiting you?
Monte Carlo solves data monitoring problems in two main areas: 1. Minimizing Data Downtime; 2. Maximizing Data Availability. Monte Carlo minimizes data downtime by notifying data engineers of potential issues that might disrupt the flow of data in the pipeline. Maximizing data availability by increasing the ability to deliver high quality throughout the data lifecycle.
Monte Carlo is the perfect companion for Analytics Engineers
What do you like best about the product?
The UI is almost perfect, and also the backend: incidents, catalog, lineage. All the content is really good. It helps a lot when you are debugging issues, and it has been a great tool to train and onboard others. The support team is great, and I love that.
What do you dislike about the product?
There's not a Jira integration yet, and it makes tracking a bit harder. However, there's some features that you can use to keep incidents and tickets tidy, you can use comments and also the API (and build reports on your own).
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us identify and track incidents of different types: volume, field health,freshness, schema changes, custom validations and others. It alerts you when there are issues, and helps you with some details and hints about the issue itself, and the related items (that is: tables, views, looker views, Looks and Dashboards).
Data observability without the hassle of rolling your own solution
What do you like best about the product?
I used to work for a company where we had a WHOLE TEAM dedicated to establishing observability on the datasets we built and maintained. The solution consisted of ad-hoc queries and home-grown tooling.
Enter Monte Carlo, which is a revelation in many ways. Our team likes that Monte Carlo makes establishing data observability a matter of configuration as opposed to stitching together disparate systems and queries to make it happen. The feature that ties it all together is the granular lineage functionality, which allows us to understand the health of our data and most importantly, understand who/what are impacted when something goes wrong upstream in our data stack.
The Product and Customer Success group are, in my opinion, the key to why we have had such a great experience with Monte Carlo. They've been so helpful in keeping us engaged, teaching us best practices, and receiving product input that actually gets implemented as new features!
Enter Monte Carlo, which is a revelation in many ways. Our team likes that Monte Carlo makes establishing data observability a matter of configuration as opposed to stitching together disparate systems and queries to make it happen. The feature that ties it all together is the granular lineage functionality, which allows us to understand the health of our data and most importantly, understand who/what are impacted when something goes wrong upstream in our data stack.
The Product and Customer Success group are, in my opinion, the key to why we have had such a great experience with Monte Carlo. They've been so helpful in keeping us engaged, teaching us best practices, and receiving product input that actually gets implemented as new features!
What do you dislike about the product?
I think the Monte Carlo UI could use a refresh. It's not the prettiest, although we care much more about functionality over looks. We're also hoping for more programmatic ways to control Monte Carlo in terms of how we configure observability. The point and click functionality of Monte Carlo is great for broader data observability considerations, but there are times where we'd like to make multiple config changes and save the clicks at the same time (Good example: turning off monitoring for multiple tables).
What problems is the product solving and how is that benefiting you?
Understanding the health of our data is crucial to everyday decision making using the data that our stakeholders rely on. Understanding the health of our data helps us to ensure data quality.
Monte Carlo Review
What do you like best about the product?
Ease of setting up a POC with clearly defined objectives and KPIs. Very quick onboarding once we moved forward with implementation and immediate, out-of-the-box features such as taps into Snowflake and Looker. Monthly touch points with the Customer Success team to ensure the product is being utilized properly and exposure to various features available within Monte Carlo.
What do you dislike about the product?
No real negatives to mention. The Monte Carlo team did a great job in facilitating integration.
What problems is the product solving and how is that benefiting you?
We now have an easy way to trace lineage of our data where we can understand how objects in DB tie into our Dashboards in Looker. We also get observability with freshness and schema monitoring coming into our Slack channel where we have a set of eyes on the alerts.
Monte Carlo's Data Reliability tool is really helping us build trust around data.
What do you like best about the product?
Automatic field health monitoring is a great feature, we can detect issues we wouldn't be aware of without Monte Carlo.
Monitoring volume spikes and drops also helps us identify a lot of "human invisible" issues.
Detecting freshness issues is also really useful for data engineers and data analysts to avoid data downtime to end users.
We managed to scale business rules testing by connecting Monte Carlo SQL custom monitors directly to Slack, to the right people.
Overall, fast and great results after using the tool for only 3 months.
Monitoring volume spikes and drops also helps us identify a lot of "human invisible" issues.
Detecting freshness issues is also really useful for data engineers and data analysts to avoid data downtime to end users.
We managed to scale business rules testing by connecting Monte Carlo SQL custom monitors directly to Slack, to the right people.
Overall, fast and great results after using the tool for only 3 months.
What do you dislike about the product?
UX is not adapted to non-tech users yet in my perspective, that could be a possible improvement for the future, as Business Analysts might also become heavy users of this tool.
What problems is the product solving and how is that benefiting you?
1. Monitoring data
2. Connecting data issue alerts to appropriate data owners
3. Finding issues that are "invisible" to the human eye, or issues that would take too much effort to an analyst to detect
2. Connecting data issue alerts to appropriate data owners
3. Finding issues that are "invisible" to the human eye, or issues that would take too much effort to an analyst to detect
Observability With a Lot of Potential
What do you like best about the product?
Being able to see data assets and the data flowing through them is super valuable for any team requiring high-quality data operations. The team supporting it is top-notch, they really care about hearing feedback and improving their product. That customer focus is also evident in the feature velocity, we've been impressed with new features and UI updates they have released recently.
What do you dislike about the product?
There could be more integrations with other tools in the modern data stack. The BI lineage could be improved with Looker. The data discovery experience could be improved as well.
What problems is the product solving and how is that benefiting you?
Ensuring stakeholders have the right data at the right time is crucial for us to respond quickly and meet our business goals. Lineage and out-of-the-box alerting are critical here.
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