Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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Smart way to stay up to date with data quality issues without writing a single test or query.
What do you like best about the product?
I love the automated AI detection of quality issues that just come to me without even having to think where and how to start monitoring my data or writing tests or queries to look for issues, I love how it all ends up in one place (ETL issues, dbt failures, database schema changes), the easily accessible field and table lineage that help me understand where some issues come from - or even help to model data and refactor old models.
What do you dislike about the product?
The only concern is the load on our database and, therefore, increased costs but after a small duscussion with the Mc team we were able to improve this too by tuning down the frequencies these checks run agains our db.
What problems is the product solving and how is that benefiting you?
Monte Carlo clearly shows impacted buisness reports in each of the detected incidents while also providing a degreee of importance of the table and its impact downstream, and allows us to give a heads up to the stakeholders early on when anything unexpected happens.
Great tool which covers our requirements
What do you like best about the product?
- high configuration abilities
- many predefined monitors
- usage of artificial intelligence
- monitors as code
- great support and collaboration with the MC team
- many predefined monitors
- usage of artificial intelligence
- monitors as code
- great support and collaboration with the MC team
What do you dislike about the product?
- no possibility to define groups for notifications purposes
What problems is the product solving and how is that benefiting you?
- less own monitoring implementation necessary
- self-learning observability moves the monitoring to the next level
- self-learning observability moves the monitoring to the next level
Ensuring Data Reliability with Monte Carlo
What do you like best about the product?
The most helpful aspect of Monte Carlo is its real-time data quality issue detection and resolution, which ensures accurate and reliable data. The platform is easy to use and provides advanced algorithms and machine learning capabilities for identifying and resolving data anomalies and outliers. Monte Carlo's customer support is responsive and helpful in addressing any issues that users encounter.
What do you dislike about the product?
Sometimes I have experienced occasional issues with data processing and the platform's user interface, although these issues seem to be infrequent and have been promptly resolved by the Monte Carlo support team.
What problems is the product solving and how is that benefiting you?
1. automatically detecting and resolving data quality issues in real-time
2. saving time and preventing costly errors that can arise from using inaccurate or incomplete data
3. improving overall data quality by identifying data anomalies and outliers
4. helping businesses become more data-driven and better equipped to make informed decisions based on high-quality data
2. saving time and preventing costly errors that can arise from using inaccurate or incomplete data
3. improving overall data quality by identifying data anomalies and outliers
4. helping businesses become more data-driven and better equipped to make informed decisions based on high-quality data
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 an automated proactive fail-safe and data trust using the 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 Anomaly
Certifying Data - Data Trust
Data Lineage to show the full impact of Anomaly
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.
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