Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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MC is the best AI tool I have never used before for Data monitoring.
What do you like best about the product?
The default custom monitors basically the freshness and volume anamolies of the tables which are used to investigate the data pipelines and data laods on the daily basis.
What do you dislike about the product?
looking for more filtering options and investigate assets for past one year of availabilty. this would help to check teh data paterrns in different time period of the fiscal year.
What problems is the product solving and how is that benefiting you?
Montecarlo provides the details of data loads /pipelines broken with any business area realted tables. Also the details of schema changes at the sources helps to proactively enhance the the DW tables as needed.
MC Review
What do you like best about the product?
It's ability to alert quickly, accurately, and easy to set up.
What do you dislike about the product?
I hope there is a function that Monte Carlo can write table into snowflake.
What problems is the product solving and how is that benefiting you?
It is solving the data quality issue coming from vendors, alerting any data pipeline issues(where etl fail to run or abnormality) and also it alert the abnormal business activity as well.
Perfect Data observality tools.
What do you like best about the product?
This is best Data observality tool which helps to represent correct data in all our dashboards through it anomalies.
What do you dislike about the product?
It's UI is little confusing at the initial stage.
What problems is the product solving and how is that benefiting you?
It solving the issue of correct data in datalake. Which alternative make sure the perfect data in all the dashboard and multiple data science models.
Using Monte Carlo as a member of a central data platform team, evangelising data quality
What do you like best about the product?
- Wealth of features in MC that not only assist in observability but also in exploring data & lineage of models
- It serves as a user-friendly UI for personas outside of core data teams. Low barrier of entry.
- Easy setup for integrations (Slack and dbt)
- The Slack posts from MC to recipient channels are awesome
- It serves as a user-friendly UI for personas outside of core data teams. Low barrier of entry.
- Easy setup for integrations (Slack and dbt)
- The Slack posts from MC to recipient channels are awesome
What do you dislike about the product?
- observability issues not solved if some users/recipients are not engaged. Accountability must be followed up with an in-house incident strategy and commitment from all. This requires a lot of evangelism work around it
- sometimes the UI can seem bland after some time because everything looks so similar. Not much to distinguish main pages of the tab selections other than inherent feature differences
- filtering on incidents can be buggy when revisiting the page after some time (I.e. stuck on a past time range, and then needing to press "clear filters")
- severity, a core concept of MC incidents, is not actually enforced. It is too optional.
- Would be great to have integrations to other BI tools that display data quality metrics across the linked exposures. I.e. if Looker, data quality metrics on the assets linked to each of the explores/dashboards. Called be called by UI or available as a downloadable report
- sometimes the UI can seem bland after some time because everything looks so similar. Not much to distinguish main pages of the tab selections other than inherent feature differences
- filtering on incidents can be buggy when revisiting the page after some time (I.e. stuck on a past time range, and then needing to press "clear filters")
- severity, a core concept of MC incidents, is not actually enforced. It is too optional.
- Would be great to have integrations to other BI tools that display data quality metrics across the linked exposures. I.e. if Looker, data quality metrics on the assets linked to each of the explores/dashboards. Called be called by UI or available as a downloadable report
What problems is the product solving and how is that benefiting you?
Moving the issue of "data errors" closer to those who produce them and are accountable for them. Instead of the communication of existing errors a task only of the core data teams
Amazing tool for data observation and creation of different monitors as per tables behavious
What do you like best about the product?
Amazing features availble to create monitors using UI direct
What do you dislike about the product?
Its frequency to update table data according to actual table is too slow, It can be increased
What problems is the product solving and how is that benefiting you?
We were facing issue for Data science job as there is change in source database and due to this table data/schema was changing so using this tool we able to create custom monitors which alerts us and we makes changes accordingly
The best Data observability tool for the data quality monitoring.
What do you like best about the product?
It provide centralised view for all your important tables using schema, lineage, freshness, volume anomalies so that we make sure correct and updated data is reflecting in dashboards. it provide Custom as well as machine generated rules.
What do you dislike about the product?
The UI is little confusing at the starting.
What problems is the product solving and how is that benefiting you?
Accuracy of the data that we are ingesting and going to use in different application. understand the which part of the pipeline is broken or slow.
Monte Carlo the great that observability tool
What do you like best about the product?
I like the Monte Carlo concept and a working product. The pre-sales and implementation have been very professional and the staff has been very helpul.
What do you dislike about the product?
There are no negative aspects about Monte Carlo that I can think of right now.
What problems is the product solving and how is that benefiting you?
Monte Carlo is enabling us to foremost build at feedback loop with the product teams who own the data sources so they know straight away if something is wrong with quality of the data they provide. It also enables data scientists to check if there is something wrong with the data in their models.
Tool that made Data Quality checks accessible & team that made Data Quality measurable
What do you like best about the product?
Monte Carlo made data quality checks no longer a responsibility of the Data engg teams, Product owner can also create monitors to track potential data issues themselves rather than relying dev teams.
Monte Carlo is one-tool solution to conduct your impact analysis and root cause analysis of all data quality issues.
Monte carlo aims to continuously improve the ways on how we measure Data Quality of our data
Monte Carlo is one-tool solution to conduct your impact analysis and root cause analysis of all data quality issues.
Monte carlo aims to continuously improve the ways on how we measure Data Quality of our data
What do you dislike about the product?
Documentation on the Monitors being used could improve. Whilst they have the monitors information available for starting with the tool, catering to advanced users need to be worked upon
Mapping monitor to a given data quality use case ( medium to advance complexity) is a trial and experimentation process.
Mapping monitor to a given data quality use case ( medium to advance complexity) is a trial and experimentation process.
What problems is the product solving and how is that benefiting you?
With data issues, its always about when its identified and who spots it. Before Monte Carlo, it was manual checks and data quality dashboard that someone has to execute daily and/ or it was the End -user reporting data errors. With Monte carlo, be it their automated monitors or custom monitors, checks are scheduled and team is notified whenever there is an incident. Thereby freeing time for developers to work on development and allowing data owners to proactively place Information notes for the impacted end-users.
Monte Carlo - Best data handling tool which reduce human efforts
What do you like best about the product?
Best data handling tool, UI is user friendly
What do you dislike about the product?
User Access settings can be improved more
What problems is the product solving and how is that benefiting you?
Schema Changes at source side, Volumn Anemalies
Great and useful data monitoring system
What do you like best about the product?
I like the fact you can monitor different tables and views, see the lineage of your tables.
have built in monitors like data freshness and volume, but also the ability to add custom tests.
have built in monitors like data freshness and volume, but also the ability to add custom tests.
What do you dislike about the product?
I would like better fitted integration to email.
sometimes you get false positives.
sometimes you get false positives.
What problems is the product solving and how is that benefiting you?
-monitoring data quality
-finding issues in the data fast
-being able to add your own tests.
-finding issues in the data fast
-being able to add your own tests.
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