Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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Excellent tool for both out of the box and custom monitoring
What do you like best about the product?
The depth of the types of monitors you can setup on the data is excellent, also with so much done based on ML it ends up being tailored to the data and spots changes that would normally go under the radar or only be alerted to us by the users of our data.
What do you dislike about the product?
The alerts we get into Slack can be a little too verbose, we want coverage across all of our assets, but this means we get a very busy channel.
What problems is the product solving and how is that benefiting you?
Identity data quality issues with our data warehouse as quickly as possible so that we can resolve before they have impact to our users.
Great data quality monitoring/alerting tool
What do you like best about the product?
Foundational data checks available out of the box (freshness, volume) and easy to set-up custom monitors.
Lineage & Performance sections are a big plus.
Lineage & Performance sections are a big plus.
What do you dislike about the product?
Deeper integration with other tools (like airflow and DBT) would allows us to have troubleshooting sessions in one place (MC).
What problems is the product solving and how is that benefiting you?
Spot data anomalies and get alerted.
Super easy to implement and use, great monitoring for data assets and pipelines
What do you like best about the product?
Monte Carlo is ridiculously easy to use. Implementing monitoring in the datasets is a few mouse clicks and the machine learning algorithm picks up the patterns in the datasets. The default monitoring (row count, freshness monitor, query logs, schema changes) are exactly what I need for most of my datasets. It's so easy to learn to use! I've managed to implement custom sql monitoring and tests without too much consulting of the manual as MC is really intuitive. I use it in all of my datasets for daily monitoring.
The slack integration and setting up "Audiences" for any alerts is quick and easy, and I love that you can send test alerts to make sure things are working
The slack integration and setting up "Audiences" for any alerts is quick and easy, and I love that you can send test alerts to make sure things are working
What do you dislike about the product?
I'd like to be able to get alerts that output variables dependent on the issue, but that's not something I've managed so far. I haven't reached out for help though, so it could be a me issue!
What problems is the product solving and how is that benefiting you?
Monte Carlo is enabling me to monitor countless data assets, deal with problems before they affect downstream pipelines, and alerting me to issues before users have the chance! It gives me the confidence that my data products are functioning correctly and that I'll be alerted to any issues.
Great tool and concept; needs some added functionality
What do you like best about the product?
I think Monte Carlo is a great way to monitor data issues and I love the "built-in" freshness/volume anomaly monitors on any tables added to Monte Carlo.
What do you dislike about the product?
We are using the Monte Carlo product to monitor our BigQuery tables. I have chatted with Monte Carlo support about this before and put in a ticket; but it would be great if we were able to set variables within Monte Carlo monitors (we wanted to use a list within the monitor in order to take advantage of partitioning in BigQuery, as BigQuery does not support dynamic partitioning and thus a CTE would not use partitioning correctly).
Scenario:
Using a list within a Monte Carlo monitor results in failure. The MC monitor simply takes the first output written in the monitor (the result of setting the list) and considers that as the monitor. The rest of the code in the monitor (after the list is set) is not considered.
You can see the ticket or contact me for additional details/explanation.
Additionally, I think it would be useful if there were more automated monitors (for example, you could set up an automated monitor so that for ANY anomalous value in the table, the monitor is triggered).
Scenario:
Using a list within a Monte Carlo monitor results in failure. The MC monitor simply takes the first output written in the monitor (the result of setting the list) and considers that as the monitor. The rest of the code in the monitor (after the list is set) is not considered.
You can see the ticket or contact me for additional details/explanation.
Additionally, I think it would be useful if there were more automated monitors (for example, you could set up an automated monitor so that for ANY anomalous value in the table, the monitor is triggered).
What problems is the product solving and how is that benefiting you?
Monte Carlo is helping alert us to issues with data quality and freshness. It also helps the data scientists on a connected team be alerted to changes in the distribution of consumers we have purchase data for.
Data quality checks
What do you like best about the product?
The montecarlo is giving me a lot of posibilities in terms of data quality. I can setup the notifications, create a groups of people and send them a notification if something failes
What do you dislike about the product?
As the user of google chat I'm realy anoyed that I need to use emails. For me the best way for alerts will be a direct message to a google chat group. The best way will be use the webhooks that google is providing
What problems is the product solving and how is that benefiting you?
The montecarlo can compare few datasets and can send me a notification if I have less data or more. It is helping me to not make a huge mistakes for example it will send me the aler if the data from the table has been droped
Monte Carlo is a game changer for our team's efforts to automate compliance controls
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
Monte Carlo is providing greater confidence in our data quality, highlighting us of pattern-based opportunities, and alerting us of user-defined regulatory compliance adherence and investigation needs. Monte Carlo covers core data governance, enables insight to data-driven controls and related change management, and is generally leveling up our approach driving action with data across a variety of dimensions.
The data alerts are easy to use and I love the assets tab!
What do you like best about the product?
The asset tab / data catalog is really good! I love how I can directly look at how frequent the table is updated and how many new rows every update have. I also love the fact that we can trace down the upstream and downstream queries/tables. This is really useful cause it allows me examine the definition of each column without having to figuring who owns the query and where I can find the definition.
What do you dislike about the product?
The experience is good overall. One thing I would note that I use the custom SQL a lot. Part of the reason is the smart alert sometimes can output unpredictable alert where it is more predictable with a threshold in custom SQL. Also, it is unclear to me if marking the alerts as "expected/no action needed/etc." feeds into the algo and makes the alert better.
What problems is the product solving and how is that benefiting you?
The data catalog helps me trace down the upstream and downstream tables and allows me to check the definiton of the columns.
The functionality that shows the accounts querying the table is also very helpful when we want to migrate/deprecate the table and we can pin down who is still using it.
The functionality that shows the accounts querying the table is also very helpful when we want to migrate/deprecate the table and we can pin down who is still using it.
A very strong platform with multiple data capabilities
What do you like best about the product?
the monitoring being customizable and the automatic monitoring providing comprehensive data vhanges overview
What do you dislike about the product?
the ui is difficult to understand at first
What problems is the product solving and how is that benefiting you?
checking when there are unexpected data changes
Difficult to set up complex monitors
What do you like best about the product?
Easy to set up simple monitors; easy to acknowledge alerts from slack
What do you dislike about the product?
Difficult to create more complex monitors
What problems is the product solving and how is that benefiting you?
We want to be alerted of anomalies in our data
Recommend Monte Carlo for data anomaly checks
What do you like best about the product?
Anomaly detection monitors, especially for fields with high cardinality, are great! They have often caught anomalous NULL values or unseen data before, which has led us to identify bugs.
What do you dislike about the product?
Some ML-based monitors that detect variations in time series are not full accurate.
What problems is the product solving and how is that benefiting you?
Ensuring data quality, identify data anomalies, setting up regular SQL monitors to run custom checks against data
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