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I've been working as a dwh developer at trendyol and we've been using the product for 1.5 year.
What do you like best about the product?
To be able to learn the incidents in our tables before our customers and to see the volume and freshness details in the UI. Easily receive notifications via Slack.The audience structure facilitates the notification process.customer support is very fast
What do you dislike about the product?
The domain section is manual and difficult to read. Not being able to see the history in case any data is deleted.We receive too many alarms from some notifications and cannot prevent them. Like normalized messages coming in the thread.
What problems is the product solving and how is that benefiting you?
When an incident occurred on our tables, we could not be informed before the customers. For example, the last modified date of a table may have passed or the flow may have stopped and new data may not have been written to the table. Here MC warns us early with the thresholds he has learned over time.
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Monte Carlo has been a game-changer for the whole company in terms of data governance.
What do you like best about the product?
The table lineage feature makes the Data Engineer and Data Analyst team members workflow much more efficient and the whole data team feels more confident in their knowledge of the data assets.
The table metrics enables the realisation of quick actions: we can decide to clean-up unused tables without worrying about unknown consequences or we can focus on optimising the tables based on the importance score. As a Data Engineer, we simply could not have optimosed our costs and improved our platform without the help of Monte Carlo.
The customer support is always swift in their response and also our concerns are addressed without hesitation. We have received exceptional support especially from Neil Gleeson, but also from the development team.
It was very easy to integrate with our warehouse and we were able to customise the access level quickly and efficiently
The table metrics enables the realisation of quick actions: we can decide to clean-up unused tables without worrying about unknown consequences or we can focus on optimising the tables based on the importance score. As a Data Engineer, we simply could not have optimosed our costs and improved our platform without the help of Monte Carlo.
The customer support is always swift in their response and also our concerns are addressed without hesitation. We have received exceptional support especially from Neil Gleeson, but also from the development team.
It was very easy to integrate with our warehouse and we were able to customise the access level quickly and efficiently
What do you dislike about the product?
Due to the product being continously developed, we experienced some minor bugs or some features not working as intended in the beginning. These are mostly frontend related bugs, not so distruptive of the core workflows that we use Monte Carlo for.
The extensive useage of the product can lead to aleart-fatigue and false positive alrerts that can be distruptive of the day-to-day workflow. We have not been able to properly adjust our workflows to react to MC alerts, there is a high overlap of the MC alerts with our other alerting systems. The false positives - although not comparebale to true positives - were causing some confusion and decreased trust in MC alerts.
We could benefit from more customizability: In some cases we have a broken lineage caused by pipelines behaving in ways Monte Carlo cannot detect properly. If we had the option to customise the lineage we could fill these gaps without the need to adjust our pielines or Monte Carlo to build a complex extra feature.
The extensive useage of the product can lead to aleart-fatigue and false positive alrerts that can be distruptive of the day-to-day workflow. We have not been able to properly adjust our workflows to react to MC alerts, there is a high overlap of the MC alerts with our other alerting systems. The false positives - although not comparebale to true positives - were causing some confusion and decreased trust in MC alerts.
We could benefit from more customizability: In some cases we have a broken lineage caused by pipelines behaving in ways Monte Carlo cannot detect properly. If we had the option to customise the lineage we could fill these gaps without the need to adjust our pielines or Monte Carlo to build a complex extra feature.
What problems is the product solving and how is that benefiting you?
Monte Carlo enables data-driven business teams to increase their supervision over the data that they are working with. This means that they get more insight in how their data is process upstream: they can understand issues quicker and approch the data team with clearer instructions. Setting up alerts helps them react to problems faster and their confidece of the data quality also increases. Since Monte Carlo is user frendly and customisable, they can potentially react to business problems they had not have the capability to monitor before. Another aspect is that Monte Carlo creates an "interface" between business and data teams. Business teams get a better view onto the "data product" that data teams offer, which increases the cooperaation and understanding between the two domains.
For data teams Monte Carlo solves also solves other problems, namely the governing capability over the data assets. With hunderds of pipelines and data assets in the thousands, it is easy to lose sight of what assets are important and what is the relation between these assets. The generated lineage graph is a game-changer for data teams which reduces the time to debug and understand certain processes significantly. It also helps optimise the data platform: we can confidently clean up unused assets to save costs and reduce noise. Data teams can also resove issues that previously went undetected which saves many hours spent debugging from both business and tech side.
For data teams Monte Carlo solves also solves other problems, namely the governing capability over the data assets. With hunderds of pipelines and data assets in the thousands, it is easy to lose sight of what assets are important and what is the relation between these assets. The generated lineage graph is a game-changer for data teams which reduces the time to debug and understand certain processes significantly. It also helps optimise the data platform: we can confidently clean up unused assets to save costs and reduce noise. Data teams can also resove issues that previously went undetected which saves many hours spent debugging from both business and tech side.
Data Governance with MonteCarlo
What do you like best about the product?
-> table & field lineage
-> out-of-the-box monitors
-> highly customizable monitor options
-> out-of-the-box monitors
-> highly customizable monitor options
What do you dislike about the product?
-> limited to BQ & BI tools. Integration with GCS/PubSub/Dataflow (or equivalent services) would definetely be a plus
What problems is the product solving and how is that benefiting you?
MonteCarlo is helping with monitoring our airflow pipelines and sending alerts to a dedicated channel.
Volume/freshness monitors help not to miss important issues.
Lineage tracing is useful in following data transformations.
The insights reports (like clean-up suggestions) help with cleaning the datawarehouse periodically.
Volume/freshness monitors help not to miss important issues.
Lineage tracing is useful in following data transformations.
The insights reports (like clean-up suggestions) help with cleaning the datawarehouse periodically.
Jelena Mataija - Vroom
What do you like best about the product?
Overall a really nice tool to monitor data quality and validity.
Setup is quiet easy enough if you prepare in advance properly, where lovely support comes along and they are always ready to help and to review if needed.
Machine learning program can detect if data hasn’t been received on frequent time giving us time to focus on other things and not to monitor our pipelines constantly. As well as slight hints in data quality changes which can come in handy.
Customised queries are a nice touch and implementation is endless.
For daily data ingestions and quality check this is a nice tool to have since it will alert you on time if anything "fishy" is going on giving you time to focus on other things.
If set up correctly there are dashboards that can be shared with different teams throughout the organisation for data monitor and for some internal audits as well.
Setup is quiet easy enough if you prepare in advance properly, where lovely support comes along and they are always ready to help and to review if needed.
Machine learning program can detect if data hasn’t been received on frequent time giving us time to focus on other things and not to monitor our pipelines constantly. As well as slight hints in data quality changes which can come in handy.
Customised queries are a nice touch and implementation is endless.
For daily data ingestions and quality check this is a nice tool to have since it will alert you on time if anything "fishy" is going on giving you time to focus on other things.
If set up correctly there are dashboards that can be shared with different teams throughout the organisation for data monitor and for some internal audits as well.
What do you dislike about the product?
You have to have a dedicated people to maintain the monitors and constant update since it is a mechine learning program, meaning you cannot get lazy with it.
Ocasionally it can overwellm a database resources but than again it depends on your company organization.
Ocasionally it can overwellm a database resources but than again it depends on your company organization.
What problems is the product solving and how is that benefiting you?
For us currently most used scenarios are ones where we get alert if our data volumes have been changed. If we do not expect it than this is a good signal that our pipelines are late or broke, often those that are coming from third party or API's.
Also several custom monitors are in place to track duplicates in our data, fluctuations in group volumes etc.
The ones where we check for unique values are really helpful, they can give us a heads up if we need to implement some other changes in our transformation models.
Also several custom monitors are in place to track duplicates in our data, fluctuations in group volumes etc.
The ones where we check for unique values are really helpful, they can give us a heads up if we need to implement some other changes in our transformation models.
MC helps us deliver better results more efficiently.
What do you like best about the product?
It is a great tool for getting a general high level overview of the state of our data sources and pipelines.
Automated default monitors (like volume and source freshness anomaly detectors) are also working well and a great resource for us!
Automated default monitors (like volume and source freshness anomaly detectors) are also working well and a great resource for us!
What do you dislike about the product?
Field lineage could be improved, although that's a hard problem to solve.
Tiny UI issues/small bugs from time to time.
More visibility to their platform vision & how to leverage more and better Monte Carlo to its full extent
Tiny UI issues/small bugs from time to time.
More visibility to their platform vision & how to leverage more and better Monte Carlo to its full extent
What problems is the product solving and how is that benefiting you?
Source freshness and volume anomaly alerting; Monitoring different sets of data and alerting us on custom-defined anomalies;
Great post production data quality tool.
What do you like best about the product?
Full confidence about my data quality. After applying monitors on top of my datasets, I can rest assure that the data is correct and accessible.
Moreover, our customer support is good when needde.
Moreover, our customer support is good when needde.
What do you dislike about the product?
Slack notifications payload design - In case I want to create a custom monitor and send an "advanced" and more informative payload, it's not that easy and accessible.
What problems is the product solving and how is that benefiting you?
Data freshness - be aware of latency so we'll be able to notify our data consumers. Data anomalies, Schema changes, nulls ext. - Monte helps us be in control over our hundreds of data assets, so we can focus on the most needed tasks and bugs.
Moreover - The slack integration with the user-set statuses helps us understand which incidents is being taken care of already, and which still requires our attention.
Moreover - The slack integration with the user-set statuses helps us understand which incidents is being taken care of already, and which still requires our attention.
End-to-end easy visibility of the data
What do you like best about the product?
Monte Carlo helps us to identify any potential inconsistency in the data framework at a quick glance. It helps flag cases that would be difficult to identify otherwise
What do you dislike about the product?
No major things, though after using other software to alert on inconsistencies with the business, I think that would be usefult that MC allows notification to contain tables with the hits that have created the alert.
It is useful for the business to easily check the alert driver and some columns that may be useful for them
It is useful for the business to easily check the alert driver and some columns that may be useful for them
What problems is the product solving and how is that benefiting you?
Analyse big pipelines end to end
Monte Carlo helps us in Data Observability
What do you like best about the product?
We get alerts from Monte Carlo as per schema change, null pct change, etc, which gives us insights of data changes of important tables.
What do you dislike about the product?
Sometimes we receive alerts of tables not in our domain.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps monitor changes of each specified attribute, which is almost impossible to do manually.
Monte Carlo let me control the chaos I have in my database
What do you like best about the product?
The frequency of the new super-useful features release and the ease of use over other DQ tools.
What do you dislike about the product?
I'm looking forward for better incidents management, especially filtering, grouppping and navigation.
What problems is the product solving and how is that benefiting you?
With monte carlo we can provide a quicker response on the data quality issues to our end users. Previously data quality checks were either not implemented (hence we had to check our data manually) or were distributed in a various sets of custom scripts that were not easy to run, maintain and understand.
Automated and evolving insights on your Data Mesh - Data Products
What do you like best about the product?
They are innovative and every month, I'm getting a new feature on my dashboard. The intergation with other Data Engineering tools is great. Monitoring as a Code or UI based monitoring management is very easy. The team is very responsive in term of customer support either for new feature or bug fixes.
What do you dislike about the product?
The naming conventions are a bit un orthodox or confusing at times.
I'm waiting for more evolved dashboarding features.
I'm waiting for more evolved dashboarding features.
What problems is the product solving and how is that benefiting you?
I'm able to monitor data availablity and qulaity matrics very easily. Do get alerts and automated management on the same.
- It's help me generate/capture SLIs on My Data Products and share them with stakeholders and potential consumers of my data.
- It's help me generate/capture SLIs on My Data Products and share them with stakeholders and potential consumers of my data.
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