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Monte Carlo Data Collector helps you to know about your Data and its behavior.
What do you like best about the product?
Easy to connect to different Data Sources
What do you dislike about the product?
It is a little tricky to upgrade in Terraform Infrastructure
What problems is the product solving and how is that benefiting you?
Understand the usage and behavior of our different data repositories.
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Great data monitoring solution
What do you like best about the product?
One place to find out everything about assets - documentation, quality, freshness, among other things.
What do you dislike about the product?
Some UI bugs can confuse the user about the status of the table.
What problems is the product solving and how is that benefiting you?
The most critical problem Monte Carlo solves is that it reduces data downtime. It lets users know about quality issues that wouldn't surface without it.
Business is able to trust the data a little more with Monte Carlo monitoring the quality.
Business is able to trust the data a little more with Monte Carlo monitoring the quality.
Must-have for your Data
What do you like best about the product?
- easy notification set up with different channels integration, including Slack or PagerDuty
- out-of-the-box ready solutions to track schema changes, volume and others
- dashboard and incident tracker are really nice
- custom monitors and the possibility to set it up through 'monitors as code'
- out-of-the-box ready solutions to track schema changes, volume and others
- dashboard and incident tracker are really nice
- custom monitors and the possibility to set it up through 'monitors as code'
What do you dislike about the product?
Some minor things like Incidents or monitor filtering, but nothing significant.
What problems is the product solving and how is that benefiting you?
It helps us with data quality monitoring. Thanks to Monte Carlo, we can predict problems earlier and identify the root cause faster.
Monte Carlo is game changing for our data team
What do you like best about the product?
Monte Carlo FINALLY allows us to see the overall health of our data operations in one spot. Through their SQL-based monitors, we can surgically instrument different parts of our various pipelines and view the trend of data changes over time. Their field health monitors are also fantastic as they often magically detect issues we would have never thought to explicitly instrument. We've also found it extremely useful to review our Monte Carlo dashboards as part of our team's standup ritual- this practice has given us a much better understanding and appreciation of the correlation between releasing new models and impacting downstream systems.
What do you dislike about the product?
Monte Carlo uniquely solved a specific need and problem for our organization, so there have been no noticeable friction or pain points. Since Monte Carlo has become our team's predominant data tool, having data dictionary functionality and product directly integrated into Monte Carlo would be convenient since we already use it so much. We'd much rather see an integrated product rather than jump to another tooling system.
What problems is the product solving and how is that benefiting you?
Having clean, reliable data is at the core of our company's ability to execute. Their data monitors give us the confidence to run a massive amount of data through our ever-changing pipelines, allowing our team to know when and, more importantly, where things are broken. Ultimately our data science team can produce more changes and value to the business while maintaining our high data quality.
very flexible & holistic approach to data quality.
What do you like best about the product?
standard & customizable quality checks, easy to use reporting. integration with atlan and slack.
I also appreciate that we've been discussing internally of functionalities that could be useful in MC, they were implemented a few weeks after feedback was shared with the MC team.
I also appreciate that we've been discussing internally of functionalities that could be useful in MC, they were implemented a few weeks after feedback was shared with the MC team.
What do you dislike about the product?
navigation could be made simpler. as you are adding new functionalities, it's normal to get complex navigation, one click per functionality. but as you improve the system, you should look into how to improve accesibility and to minimize the number of clicks required.
What problems is the product solving and how is that benefiting you?
centralization of customized data quality checks and statistics.
we have all our controls in just one place, reducing our time invested into the controls & resolution of data breaches.
we have all our controls in just one place, reducing our time invested into the controls & resolution of data breaches.
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.
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