Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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Super Dynamic Tool that has greatly improved our efficiency
What do you like best about the product?
The support and ease of use!
I really enjoyed that we could just set up our databases/schemas and things just started rolling.
I also enjoyed having the slack channel that allowed us to ask questions and get someone to meet with us on an ongoing basis.
being able to quickly identify schemas changes or delays in key assets was essential to keep us ahead of the curve and saved us who knows how many hours from having to manually set up our own testing suite.
I really enjoyed that we could just set up our databases/schemas and things just started rolling.
I also enjoyed having the slack channel that allowed us to ask questions and get someone to meet with us on an ongoing basis.
being able to quickly identify schemas changes or delays in key assets was essential to keep us ahead of the curve and saved us who knows how many hours from having to manually set up our own testing suite.
What do you dislike about the product?
I did feel like some products were introduced but considerations/documentation was a little lacking.
None of them cause me much concern because i know you will come around to getting this up-to-date at some point.
None of them cause me much concern because i know you will come around to getting this up-to-date at some point.
What problems is the product solving and how is that benefiting you?
We are primarily using it right now to tell us about the health of our data. are things changing without us knowing, is it coming in on a regular cadence, etc.
A great tool for tracking our data
What do you like best about the product?
I like the adaptability of being able to leverage out of the box checks and monitors as well as custom monitors to achieve a deeper understanding of our data as it's in motion throughout our systems. We use this tool daily in our on-call rotation to monitor our data for anomalies on a much deeper level than simple ELT failures. Once set up, it's easy to use, and delivers the right level of information with each click level in the UI. It's decreased our response times for data anomalies and allowed us to stay on top of our pipelines in ways we simply couldn't before.
What do you dislike about the product?
I wish there was a mechanism by hich we could define secondary levels of organization within a table in the out of the box functionality. It's one thing to say the number of customers in a table has increased 45%, but it's much more useful to say that a specific employer of those customers has had a 45% increase in their population. The ability to effectively edit group by statements in the data monitoring queries would take our data observability efforts from the level of technical validation to business validation (and value) really quickly.
What problems is the product solving and how is that benefiting you?
Monte Carlo augments our ETL pipelines so we can catch anomalies in the contents of our data in (nearly) real time. We're using it as the primary interface for our on-call alerting and data monitoring, and so far it's been great. Most of our ETL errors, data anomalies, and validation rule breaks flow through Monte Carlo and out to various other alerting mechanisms so our engineer on call can get valuable error information in a timely manner.
Great experience so far
What do you like best about the product?
We are able to find problems with our data and act upon it quickly.
What do you dislike about the product?
Not too sure about it. Maybe for the insght report, can you enable it even if there are no data in the report, so we can create alerts for the reports?
What problems is the product solving and how is that benefiting you?
Ensuring data quality and act upon problems in our data fast.
Great service - when used properly
What do you like best about the product?
MC is great when used effectively, especially with the new data validation features. Alerts that do not represent critical errors but rather issues in quality are very helpful when made with care and intention. Custom SQL and validations are those that seem to be the best used en-masse.
What do you dislike about the product?
Lots of monitors create excessive noise. Especially out of the box monitors and metric monitors. They are easily the biggest culprits of alert fatigure and are difficult to continue to action frequently.
What problems is the product solving and how is that benefiting you?
helping us be aware of potential non-critical issues that allow us to provide better confidence in our data - especially on the consumer end.
Great API and Monitors as Code Documentation
What do you like best about the product?
I like how thorough the API calls are documented online. I also like how the various types of output object types are explained. Furthermore, I like how customizable the monitors are to the tables and schemas that I desire. It easy to create and update a monitor to my specifications.
What do you dislike about the product?
I don't like how some of the API documentation is inconsistent. For instance, audiences are considered as "labels" occasionally and monitor names are referred to as "description". From the viewpoint of a developer, I wish there was more documentation about the API variables. Furthermore, I wish there were more comprehensive examples regarding monitor creation and updates using the API. Most examples online use Monitors as Code or the CLI but there should ideally be one query or mutation example use case for each API call.
What problems is the product solving and how is that benefiting you?
They are helping us abide by Data Governance policies to ensure that our data is accurately monitored for any anomalies or changes.
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 can alert quickly, accurately, and easily. Monte Carlo can send an alert to Slack, which everyone checks daily, allowing someone to react more rapidly.
What do you dislike about the product?
I hope there is a function that Monte Carlo can write a table into Snowflake. For now, I need to set up a dbt to create a table and an alert from Monte Carlo, which is not really convenient.
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
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