Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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Key part of our data strategy
What do you like best about the product?
1. Easy to set up,
2. Integrates nicely with Slack.
3. Solves an issue around data observability that is not currently solved by existing tools.
4. Company passionate about data and visionary.
2. Integrates nicely with Slack.
3. Solves an issue around data observability that is not currently solved by existing tools.
4. Company passionate about data and visionary.
What do you dislike about the product?
Mostly limited to big data cloud data warehouses like Snowflake, Databricks. Product still maturing from reporting perspective.
What problems is the product solving and how is that benefiting you?
Visisbility into data frshness and detects issues with data we ingest such as changes in distributions. Allows us to focus on core mission and not creating testing suites on data observability
Monte Carlo - Clearcover
What do you like best about the product?
The anomaly alerts delivered to slack are highly beneficial, but I probably use metadata and lineage information from the catalog the most.
What do you dislike about the product?
The search results delivered to the screen in the catalog. It's not very intuitive, and sometimes the table or view you would expect to appear at the top of the list is buried further down the result list.
What problems is the product solving and how is that benefiting you?
Data Discovery and stakeholder utilization of key assets. Metadata captured by MC ensures my team produces the correct solutions for our stakeholders. By sharing definitions and common query info, direct questions to my team are decreased so that we can focus on solution delivery.
We also use anomaly detection slack alerts in our daily data quality checks. While these are reactive alerts, the engineering team should be aware before stakeholders make us aware.
We also use anomaly detection slack alerts in our daily data quality checks. While these are reactive alerts, the engineering team should be aware before stakeholders make us aware.
Great customer service, UI needs a little work
What do you like best about the product?
The people are responsive and helpful. The platform has a great foundation - Monitors are pretty awesome and Catalog shows promise.
What do you dislike about the product?
I want Catalog to be amazing. It's pretty good right now, but there are some fundamental issues that are preventing me from fully committing everyone to using it.
What problems is the product solving and how is that benefiting you?
Early alerts for data problems via custom SQL monitors help to get on top of potential issues. Catalog provides a platform for data discoverability. We're building out our metadata and planning to pilot MC as a data discovery tool for the whole company in the coming months.
Effective at proactively identifying data quality issues
What do you like best about the product?
Monte Carlo keeps it simple, identifying data quality issues before they get identified by our users and data consumers. Our team can then fix issues before they become bigger problems downstream. It's easy to set up and easy to use on an ongoing basis.
What do you dislike about the product?
The out-of-the box monitors (freshness, volume, etc.) work exceptionally well, but there are still cases where we need to implement custom monitors or use other tools (e.g., dbt tests) to catch errors or data quality issues. So it's doesn't solve 100% of our data quality issues.
What problems is the product solving and how is that benefiting you?
Monte Carlo identifies data quality issues and notifies us quickly, allowing us to fix those issues before they reach our data consumers. This helps us build trust with our data consumers.
Monte Carlo clearly demonstrates the maxims stated in the Google SRE book
What do you like best about the product?
Observability into production workloads.
What do you dislike about the product?
Minor nitpick - Monte Carlo reports unexpected deletion of data any time Databricks Optimize removes underlying s3 objects
What problems is the product solving and how is that benefiting you?
The Data quality monitoring capabilities help us rethink integrity checks within the data pipeline. Observing the metadata and doing targeted notifications without setting up additional jobs.
Create Automated Proactive Fail-Safe and Data Trust using Monte Carlo Data Observability Platform
What do you like best about the product?
Get Value for Money (ROI) on investment immediately
Generate Data Trust
Very Easy to Implement
Rich Integration with Tools
Generate Data Trust
Very Easy to Implement
Rich Integration with Tools
What do you dislike about the product?
Only available for SAAS Platforms and Dashboard Tools
Workflow Access Control is not yet available
Workflow Access Control is not yet available
What problems is the product solving and how is that benefiting you?
Proactive Data Monitoring
Certifying Data - Data Trust
Data Lineage to show the full impact of Anamoly
Certifying Data - Data Trust
Data Lineage to show the full impact of Anamoly
Monte Carlo's value grows over time
What do you like best about the product?
Our use case is to create a data lake that is populated from many data sources which our team does not really understand. At the beginning, it was difficult to configure the appropriate monitors for these various products because of this. We mostly focused on the freshness monitors. We used it to keep track that the data replication into the datalake is running.
But we are finding that Monte Carlo's ML driven anomaly detection is able to give us other insights about the those data sources. In some cases, we were able to find behaviors in the data that even the team that is responsible for the data source is not aware of. A very specific example, is that there was a process that truncates tables and repopulates them on a schedule. In a sense that is expected behavior, but it is nice to be able to fully monitor and visualize it.
What I like the most is that I am able to find both expected and unexpected behaviors in the data.
But we are finding that Monte Carlo's ML driven anomaly detection is able to give us other insights about the those data sources. In some cases, we were able to find behaviors in the data that even the team that is responsible for the data source is not aware of. A very specific example, is that there was a process that truncates tables and repopulates them on a schedule. In a sense that is expected behavior, but it is nice to be able to fully monitor and visualize it.
What I like the most is that I am able to find both expected and unexpected behaviors in the data.
What do you dislike about the product?
We configured two Snowflake integrations with Monte Carlo: one against our production account and the other for the non-production account. We discovered that it is not so easy to separate the data along those domains by default, which was surprising. We needed to define custom domains and separate them out ourselves. This doesn't make sense to us. I imagine most customers also have separate production and non-production accounts that they integrate. Why isn't the integration level not a pre-built domain category by which we can separate data?
What problems is the product solving and how is that benefiting you?
We maintain a datalake the is populated from all the various products our company develop. We use various replication tools to move data to this datalake.
We primarily use Monte Carlo to monitor the changes in the datalake which indirectly tells us that the replication tools are working.
But we also are now starting to use Monte Carlo to learn about the data and the schemas. Because the data come from a variety of products/applications, they are all different. There's not a common schema. In some cases, Monte Carlo has shown us behaviors in the data that not even the source products are aware of.
We just recently went live in production and so we are only starting to see real behaviors. I'm hoping to see Monte Carlo show us other behaviors that we aren't aware of.
We primarily use Monte Carlo to monitor the changes in the datalake which indirectly tells us that the replication tools are working.
But we also are now starting to use Monte Carlo to learn about the data and the schemas. Because the data come from a variety of products/applications, they are all different. There's not a common schema. In some cases, Monte Carlo has shown us behaviors in the data that not even the source products are aware of.
We just recently went live in production and so we are only starting to see real behaviors. I'm hoping to see Monte Carlo show us other behaviors that we aren't aware of.
Great tool to aid at places you don't look at
What do you like best about the product?
Monte Carlo is a great tool to monitor data you don't watch for yourself.
It helps you catch changes in the application level that haven't been communicated enough and watch out for external interference.
It helps you catch changes in the application level that haven't been communicated enough and watch out for external interference.
What do you dislike about the product?
The BI insights are lack of details. We would like to have more of them and find more errors at the places where the management looks at reports.
We don't get enough details about changed schemas.
We get many incidents and don't know which ones are significant or which are not. Adding a significant weight to each one of those changes would be welcome.
We don't get enough details about changed schemas.
We get many incidents and don't know which ones are significant or which are not. Adding a significant weight to each one of those changes would be welcome.
What problems is the product solving and how is that benefiting you?
It helps us know if the data source got changed (or had issues) that we are unaware of otherwise.
Monte Carlo has prevented countless hours of data downtime.
What do you like best about the product?
Even if your orchestration tool sometimes catches issues, it will not detect things such as unexpected volume changes in a given table. MC gives us that across all of our tables automatically, which greatly improves our peace of mind.
What do you dislike about the product?
If you are not very diligent about your setup, or if you've built your codebase and warehouse in an unorthodox way, MC can set off false positives often and cause alert fatigue.
What problems is the product solving and how is that benefiting you?
Data observability at scale made easy.
Game Changer Governance Tool
What do you like best about the product?
As a cutting-edge data governance tool, Monte Carlo has exceeded my expectations in every way. From its intuitive user interface to its robust functionality, this tool offers a comprehensive solution for managing and governing data in a highly effective and efficient manner.
One of the standout features of Monte Carlo is its ability to detect and alert users to data quality issues in real time. This has enabled me to proactively address potential issues before they become bigger problems, saving me time and reducing the risk of errors in my data.
Another impressive aspect of Monte Carlo is its ability to track and monitor data lineage across the entire data ecosystem. This level of visibility has enabled me to easily identify the sources of my data and how it has been transformed, making it easy to trace back to any issues or errors that may arise.
One of the standout features of Monte Carlo is its ability to detect and alert users to data quality issues in real time. This has enabled me to proactively address potential issues before they become bigger problems, saving me time and reducing the risk of errors in my data.
Another impressive aspect of Monte Carlo is its ability to track and monitor data lineage across the entire data ecosystem. This level of visibility has enabled me to easily identify the sources of my data and how it has been transformed, making it easy to trace back to any issues or errors that may arise.
What do you dislike about the product?
Firstly, I would like to see improvements to the user interface. While the tool is generally easy to navigate, some of the features and functionalities could be better organized and more intuitive. Clearer labels and more streamlined workflows would make it easier to find the features I need and use the tool more efficiently.
Another area where I would like to see improvements is in the speed and reliability of the tool. I have experienced slow load times and occasional crashes, which can be frustrating and disrupt my workflow. Improvements to the tool's performance would help me to work more efficiently and effectively.
Another area where I would like to see improvements is in the speed and reliability of the tool. I have experienced slow load times and occasional crashes, which can be frustrating and disrupt my workflow. Improvements to the tool's performance would help me to work more efficiently and effectively.
What problems is the product solving and how is that benefiting you?
One of the main problems that Monte Carlo solves is the issue of data quality.. By continuously monitoring the data flow and alerting users to potential data quality issues, Monte Carlo helps organizations to identify and address these issues in a timely manner, before they become bigger problems.
Another problem that Monte Carlo addresses is the lack of visibility into the data pipeline. With many organizations using multiple data sources and transformations, it can be difficult to track the lineage of data and understand how it is being transformed. Monte Carlo provides end-to-end data lineage visibility, enabling users to understand the data pipeline and trace data issues back to their source.
Another problem that Monte Carlo addresses is the lack of visibility into the data pipeline. With many organizations using multiple data sources and transformations, it can be difficult to track the lineage of data and understand how it is being transformed. Monte Carlo provides end-to-end data lineage visibility, enabling users to understand the data pipeline and trace data issues back to their source.
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