Reviews from AWS Marketplace
0 AWS reviews
-
5 star0
-
4 star0
-
3 star0
-
2 star0
-
1 star0
External reviews
External reviews are not included in the AWS star rating for the product.
MC is a must have for every Data Engineer
What do you like best about the product?
MC gives me the ability to know at any time the status of my data, freshness, connectivity and anomalies. I catch issues much faster than before, and have better tools to understand it and fix it better and faster!
What do you dislike about the product?
The UI can use some upgrades... for example, edit options, the links between different pages, loading time, and design.
What problems is the product solving and how is that benefiting you?
Data freshness, recreating ETLs in a responsible way, alerting on most valuable tables issues.
- Leave a Comment |
- Mark review as helpful
A true data observability shield
What do you like best about the product?
I enjoy the fact we don't need to proactively set up monitors for each new table or important metric in our data warehouse. Monte Carlo by default tracks all your tables in your data warehouse.
What do you dislike about the product?
Nothing much, I think there is still room for improvement as to how to distribute the alerts and to gather feedback from your data consumers at the org.
What problems is the product solving and how is that benefiting you?
It helps us distribute the responsibilities and accountability for data observability in the org. As the business grows with its data usage and consumption allocating the observability to one data engineering group is almost impossible, some of the data issues bear for a business context, using Monte Carlo we can make sure data changes are immediately identified and shared with the data publisher and consumers across the org.
Recommendations to others considering the product:
I would recommend you build some sort of an agreement with the teams that will be helping to track the different alerts triggered by MC. It will help you get the context needed to distinguish between a false alert to something that should be investigated.
I.e. a document breaking down which schema/table should be firing alerts to the right email distribution or slack channel.
I.e. a document breaking down which schema/table should be firing alerts to the right email distribution or slack channel.
Bring data observability part of dataOps culture
What do you like best about the product?
Easy to use and no upfront investment from your engineers.
Core business data does not leave your network and only metadata is shared with the platform which makes data privacy compliance easy
No manual setup needed to see the first results
Integration with any of the monitoring tools that you have adopted for your application / cloud landscape
Excellent collaboration with the product teams at MC and new features are shipped frequently
Core business data does not leave your network and only metadata is shared with the platform which makes data privacy compliance easy
No manual setup needed to see the first results
Integration with any of the monitoring tools that you have adopted for your application / cloud landscape
Excellent collaboration with the product teams at MC and new features are shipped frequently
What do you dislike about the product?
There are not any painful experience on the platform.
What problems is the product solving and how is that benefiting you?
Early detection of data anomalies.
All incidents including data can be integrated to one tool via MC API
Ability to meet data SLAs
The cream on top is the easy to use lean Catalog that helps your power users
All incidents including data can be integrated to one tool via MC API
Ability to meet data SLAs
The cream on top is the easy to use lean Catalog that helps your power users
Recommendations to others considering the product:
Excellent tool to bring data observability into your data warehouse
showing 201 - 203