Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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MC review
What do you like best about the product?
Keeping track of the state of the DWH and trace the lineage of tables and fields.
What do you dislike about the product?
The out of distribution notifications can be a bit overwhelming especially when the anomally is a biproduct of an already known issue in the DWH.
What problems is the product solving and how is that benefiting you?
Visibility into changes in the DWH that might be a second degree plus.
Powerful yet easy to use
What do you like best about the product?
Monte Carlo has an intuitive point-and-click interface that makes it easy to get up and running quickly. But at the same time the ability for more advanced configuration and code-based featuers makes it great for power users and for just about every data quality use case that we've faced at our mid-sixed business. We use it to monitor data quality but also to track the performance of our implementation of dbt and our dbt-native tests on quality. Even though Monte Carlo serves our needs there are still many features that we're excited to take advantage of in the future like circuit breakers.
What do you dislike about the product?
While it was easy to get the platform setup initially and get some preliminary alerting going, there was a bit of a learning curve before we began to feel like we were getting the most out of even the beginning features we were implementing.
What problems is the product solving and how is that benefiting you?
We primary use Monte Carlo for alerting around data quality issues. We rely heavily on Monte Carlo's native, machine-learning based tests as well as custom tests that we have written ourselves to alert members of our data team as well as the end-users of data products when potential data issues arises.
Secondarily, we use Monte Carlo within the data team to monitor the performance of our dbt models.
Secondarily, we use Monte Carlo within the data team to monitor the performance of our dbt models.
Great product backed with excellent support
What do you like best about the product?
MonteCarlo is a great tool for observing your data, I really like the automated monitors they provide as well as the functionality to build any number of custom monitors you want. In addition, they are super responsive to any concerns we have, and generally have been able to get in touch with someone within a couple of hours.
What do you dislike about the product?
The UI seems a bit dated, and can be daunting at first to get used to. I also wish they included visualizations within their slack alerts. I'm told this is on their roadmap.
What problems is the product solving and how is that benefiting you?
Monte Carlo is solving data observability for us. We are able to leverage their technology to build automated and custom monitoring on our data and ensure production tables are always hosting the correct data.
Good data quality checking tool, can be too techical for business users
What do you like best about the product?
ML powered tool, great in picking up data anomalies
What do you dislike about the product?
Can be challenging to tune noisy monitors. Harder for the business users to add their own metrics. No suggestions for monitor candidates. No error notification if monitor configuration is failing
What problems is the product solving and how is that benefiting you?
Anomalies in data which is super hard to catch 'manually'
A simple tool to track all aspects of data quality
What do you like best about the product?
I am thoroughly impressed with Monte Carlo's data observability tool. The monitoring features it provides are exceptional, enabling us to ensure data quality with ease. This tool has been a game-changer for our business, allowing us to confidently trust our data and make informed decisions.
Additionally, the exploration tools offered by Monte Carlo are top-notch. They provide deep insights into our data, making it easy to analyze and understand trends. These tools have been invaluable in uncovering hidden patterns and optimizing our data workflows. Especially when investigating data quality issues and getting to the root cause of a bug.
Overall, Monte Carlo has ensured our data is reliable and up to date. Its robust monitoring and exploration features have empowered us to maintain high data quality standards and drive our business forward. I would recommend Monte Carlo to any organization looking to enhance their data observability and ensure the integrity of their data.
Additionally, the exploration tools offered by Monte Carlo are top-notch. They provide deep insights into our data, making it easy to analyze and understand trends. These tools have been invaluable in uncovering hidden patterns and optimizing our data workflows. Especially when investigating data quality issues and getting to the root cause of a bug.
Overall, Monte Carlo has ensured our data is reliable and up to date. Its robust monitoring and exploration features have empowered us to maintain high data quality standards and drive our business forward. I would recommend Monte Carlo to any organization looking to enhance their data observability and ensure the integrity of their data.
What do you dislike about the product?
Sometimes the monitors can be noisy and cause a lot of false positives. It takes some effort to go in and ensure the monitors are tuned appropriately. For instance, row count anomalies can be noisy when a larger addition than expected occurs, but these are frequently false positive alerts.
I would also appreciate some more options for business logic monitoring. Often times stakeholders want to be able to see if sales of a specific product are down or shipping times are creeping up. These monitors are not so much about the quality of the data but the business understanding and it would be helpful to have a more robust and scalable way to integrate some sort business logic monitors into the product.
I would also appreciate some more options for business logic monitoring. Often times stakeholders want to be able to see if sales of a specific product are down or shipping times are creeping up. These monitors are not so much about the quality of the data but the business understanding and it would be helpful to have a more robust and scalable way to integrate some sort business logic monitors into the product.
What problems is the product solving and how is that benefiting you?
Monte Carlo is solving data pipeline issues and alerting us when there is bad data that is appearing in our tables.
Extremely useful product that sometimes experiences bugs
What do you like best about the product?
Monte carlo is easy to use. It is an essential tool for our team to be able to track table and field lineage across several layers of data repositories and data products.
What do you dislike about the product?
It can be frustrating when attempting to track lineage and you experience a bug. Bugs my team has experienced are instances where lineage is incorrect, lineage within UI, API, and download does not match, and unable to download lineage for assets with too many downstreams. These issues are not quickly resolved by customer support.
What problems is the product solving and how is that benefiting you?
Monte carlo allows our team to ensure our data is fresh. We have alerts configured through the UI to trigger when anomolies occur so that our team can proactively investigate before downstream users experience an issue. Also, the ability to track lineage of assets is critical to our team.
good to get issue in early stage
What do you like best about the product?
best things about about montecarlos is that we can easily setup configruations
What do you dislike about the product?
little bit costly, it could be reduced on the basis of use
What problems is the product solving and how is that benefiting you?
montecarlo provides data issue as early stage baes on history load data
its= helps us to management and alert our data , very helpful and trusted
What do you like best about the product?
It helps us to manage and alert our data, is very helpful and trusted, and shows data in a nice and easy read way, easy use, easy implementation so team can quickly start using it
What do you dislike about the product?
i can't download more than 500 lines of data
What problems is the product solving and how is that benefiting you?
time saving - by montitoring and alerting it reduce the time spent in manual run
it distributes the work evenly between the team
it provide clear visibility
it distributes the work evenly between the team
it provide clear visibility
MC is a great tool which comfortably integrates with other tools we use
What do you like best about the product?
Integration with other tools we use
Customization abilities
Customer support via dedicated Slack channel
Customization abilities
Customer support via dedicated Slack channel
What do you dislike about the product?
Limit on query run time
Limit of the amount of queries I can run simultaneously
500 rows limit in the alerting output
Limit of the amount of queries I can run simultaneously
500 rows limit in the alerting output
What problems is the product solving and how is that benefiting you?
Automating pre-planned SQL queries to identify data discrepancies
MC benefits by running the queries on timed sessions, and with customization
MC benefits by running the queries on timed sessions, and with customization
MC user review
What do you like best about the product?
It feels solid, well thought-through and reliable. The UI looks confortable and organized. The slack integration and notifications are great. We rely on it now every day to know the health of our data. Every new issue we detect makes use create new rules.
What do you dislike about the product?
Only a very small thing. I found some wording a bit confusing when implementing a new rule. but took me 1 more minute to understand and do what I wanted.
What problems is the product solving and how is that benefiting you?
MC makes us more consiscient of othe quality of our data and processes, so we know we have something checking what's happening all the time - we don't have to do it ourselves. knowing the change in amouts of data movement or changes in the schemas or objects is very helpful.
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