Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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Great automated monitoring with room for improvement in custom monitors and documentation
What do you like best about the product?
Monte Carlo automated monitors are really useful for monitoring a large number of tables and capturing the most crucial types of errors (volume, freshness and schema changes). Having this in our company makes it really handy to detect main anomalies.
Addtionally having the possibility of configuring monitors using the UI or via API are very useful for the different stages of development.
Customer support is also great and have a great understanding about data modelling.
Addtionally having the possibility of configuring monitors using the UI or via API are very useful for the different stages of development.
Customer support is also great and have a great understanding about data modelling.
What do you dislike about the product?
Custom Monitors are still a funcionality to be improved in Monte Carlo, overtime you see new features being released and more customisation in the tool. I would appreciate a more detailed documentation about monitor configuration using better examples, different use cases and explaning important concepts like lookback days, run history and results page as well as best pratices on table structure and freshness to take the most out of the tool.
What problems is the product solving and how is that benefiting you?
Monitoring, anomaly detection and alerts on many tables being processed in a daily basis
Taking Data Observability to the next level with Monte Carlo!
What do you like best about the product?
Monte Carlo listens to their customers and is constantly adding new features to meet customer’s needs. It is a true partnership.
New features such as Notifications, Audiences, SQL rules, Tags, and Parameterized Values have allowed us to surface data issues directly to business stakeholders through Monte Carlo’s strong integration with Slack. The end result is improvements in data ownership and data quality.
Monte Carlo is a thought leader in the Data Observability space. They’ve championed metrics such as “data downtime” which is a standard we’re using to measure our success with data quality, trust and reliability.
New features such as Notifications, Audiences, SQL rules, Tags, and Parameterized Values have allowed us to surface data issues directly to business stakeholders through Monte Carlo’s strong integration with Slack. The end result is improvements in data ownership and data quality.
Monte Carlo is a thought leader in the Data Observability space. They’ve championed metrics such as “data downtime” which is a standard we’re using to measure our success with data quality, trust and reliability.
What do you dislike about the product?
I find the navigation to be difficult at times. As a result, I’m often clicking around to find functionality such as reporting or lineage.
What problems is the product solving and how is that benefiting you?
The goal is to catch data quality issues before our key stakeholders do. This is the exact problem Monte Carlo is solving for us. Monte Carlo allows us to get out in front of data issues.
OverAll Good. But, Still needs to be improved a lot.
What do you like best about the product?
Visualisation and capturings are looks good
What do you dislike about the product?
We see some limitations still to add all the filters
What problems is the product solving and how is that benefiting you?
Monitoring the data based on different dimentions i.e. Volume and recency of the data
Serious about Data Quality
What do you like best about the product?
Monte Carlo's data obsevibility, especially the freshness and volume monitors, as these are our early warning systems for data issues. Combined with these are the anomoly detection and automated alerts which makes this an awesome data quality tool.
What do you dislike about the product?
No built-in functionality for automated promotion of monitors between different environments.
What problems is the product solving and how is that benefiting you?
In lower environments early indications on volume and freshness, as well as data anomolies help resolve data issues during develop phases. These benefit the organization as it assists in detecting issues in data early in the development life cycle. In the production environment volume, freshness as well as the validation monitors assist in detecting any data quality issues, with the automated alerts. This allows for early detection of data issues before business becomes aware of it, making the data quality management an pro-active early warning system, which allows for the data stewards to pro-actively react to such issues.
Monte Carlo Review - HubSpot Analytics Engineering POV
What do you like best about the product?
* automatic set up on volume and anomaly detection
What do you dislike about the product?
* inflexible nature in variable usage (e.g. capitalization of table names)
What problems is the product solving and how is that benefiting you?
Data integrity to ensure assets have expected volume & freshness as they get produced daily.
Monte Carlo Review
What do you like best about the product?
Cohesive testing on the tool
Default test setups on new tool
Flexibility to have customized testing sql
Easy to use overall for beginners
Good dedicated customer support slack channel
Few clicks setup for new testing
Default test setups on new tool
Flexibility to have customized testing sql
Easy to use overall for beginners
Good dedicated customer support slack channel
Few clicks setup for new testing
What do you dislike about the product?
Dashboards- there is a limit of a certain number of tables. This should be increased
Ability to change WH sizes easily
Better notification strategy
Ability to change WH sizes easily
Better notification strategy
What problems is the product solving and how is that benefiting you?
It monitors the daily runs of tables, freshness, volumes etc. If any changes are made, they are quickly detected. This enables us to keep up the accuracy and maintain good quality data assets
Monte Carlo has helped us to monitor and resolve issues before our stakeholders.
What do you like best about the product?
The best part about Monte Carlo is the more you use it the more you get out of it. Training the models was definitely noisy at first, but once you give consistent feedback, the noise is much more manageable. For noisier alerts there is the event rollup feature. The UI is userfriendly as well.
What do you dislike about the product?
Monte Carlo is continuing to work on their customization. For example working with their API is a bit clunky.
What problems is the product solving and how is that benefiting you?
Monte Carlo is helping us to ensure that our data is accurate, up to date, and if our data quality slips, we get notified first. Monte Carlo monitors multiple stages in our pipeline to help us debug efficiently and point us to the best starting point for investigation.
Easy to use data tool with clean and friendly design
What do you like best about the product?
* easy to use out of box monitors and features
* clean and modern UI
* easily integrated into other platforms
* clean and modern UI
* easily integrated into other platforms
What do you dislike about the product?
* Monitors as code has a longer learning curve to understand
What problems is the product solving and how is that benefiting you?
For us, monte carlo is solving the issues of data quality and helpingus bieng proactive about our data.
Powerful Data Observability platform with an amazing support team!
What do you like best about the product?
* Extensive functionality to monitor the health of your data.
* Very good support team
* Everything you need for data observability
* Integrates with dbt.
* Very user friendly and good looking interface.
* Machine learning is used to learn the parameters of the monitors for your tables. (eg when a table is expected to be refreshed).
* Very good support team
* Everything you need for data observability
* Integrates with dbt.
* Very user friendly and good looking interface.
* Machine learning is used to learn the parameters of the monitors for your tables. (eg when a table is expected to be refreshed).
What do you dislike about the product?
* The possible API calls are limited. Specifically we are not able to set asset owners with API calls and we need a workaround.
* The performance dashboards could be improved. Often the same query is listed as multiple entries.
* The performance dashboards could be improved. Often the same query is listed as multiple entries.
What problems is the product solving and how is that benefiting you?
We are building our capabilities to observe the health of our systems. We are especially interested in the combination of issues and lineage, which help us identify the downstream dependencies that must be altered or at least made aware of upstream issues.
Quick improvements and good support, but still many bugs
What do you like best about the product?
It's improving fast, adding useful features, turning the UI, monitors and settings more intuitive and with a good support. It's now easy enough for a self-serve across the company
It's widely used
It's widely used
What do you dislike about the product?
We have a self-service of the tool across the company and we need to more easily detect expensive and useless monitors. There are a few discrepancies between insight reports, the UI and the daily digests.
We recently had an important incident that took 10 days to be solved and affected some relevant monitors
It's not intuitive at all the creation of built-in monitors (freshness, volume anomalies, schema changes)... it requires a completely different path than the custom monitors
We recently had an important incident that took 10 days to be solved and affected some relevant monitors
It's not intuitive at all the creation of built-in monitors (freshness, volume anomalies, schema changes)... it requires a completely different path than the custom monitors
What problems is the product solving and how is that benefiting you?
As a data engineer, we mostly use the freshness and volume anomalies monitors. Also, we have some SQL rules to monitor specific data quality checks
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