Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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A game changer
What do you like best about the product?
MonteCarlo has helped us achieve Observability for our pipelines, and it has made debugging/identifying root causes for incidents a lot easier. Lineage tracking for upstream/downstream dependencies as well as Freshness SLIs Monitors are extremely useful. Implementation is very straightforward for non-data teams to set up (Slack notifications are a must!). Also, the Product team is highly responsive and always open to suggestions.
What do you dislike about the product?
It has some minor UI/UX issues, but again the Product team has done an excellent work to resolved them.
What problems is the product solving and how is that benefiting you?
Lineage tracking & identifying root causes much faster
Senior Data Engineer
What do you like best about the product?
Monte Carlo gives us several features out of the box: data observability, data catalog and automated alerting on several criteria. From POC trial to onboarding, the team has been great at listening to feedback and feature requests.
What do you dislike about the product?
There are a few minor issues with the UI when doing things in bulk, but Monte Carlo is listening and assisting with performing some of those operations on their end until the features are available.
What problems is the product solving and how is that benefiting you?
We are using Monte Carlo to understand the freshness, volume, and quality of our data. We get some alerts from our ETL pipeline, but Monte Carlo alerts us when tables have not been updated on their regular schedule. These alerts have pointed us to expired credentials and stalled ETL jobs that failed silently in other systems.
Great data quality tool
What do you like best about the product?
Clearly most complete vision when it comes to automating data quality control off all startups in the market. Provides value with very little configuration.
What do you dislike about the product?
Number of possible dimensions. Would be great if you can have a group by to calculate data quality metrics per group.
What problems is the product solving and how is that benefiting you?
Automaticallly recognizing data quality issues in our datawarehouse.
Complete and intuitive
What do you like best about the product?
Very easy to set up and use, responsive development team, automates valuable testing and analysis
What do you dislike about the product?
Nothing I can think of, there have been a few UI changes lately which force you to reevaluate how you use it, but they are usually for the better and communication around it is great
What problems is the product solving and how is that benefiting you?
Database table usage, proactive analysis of schema and data quality/quantity changes, allows us to fix issues before it impacts end users
Awesome Data anamoly detection tool
What do you like best about the product?
They have a very good anomaly detection tool. The ML at the backend is superb and helps us identify any issue before business teams find it.
What do you dislike about the product?
Rules section of the tool is pretty cluttered and not user friendly
What problems is the product solving and how is that benefiting you?
Data anomaly. Early prediction of problems
Great data monitoring product!
What do you like best about the product?
The ability to see upstream and downstream dependencies of data tables. This makes troubleshooting much easier when a problem occurs. Slack integrations make it easy to monitor anomalies and data issues without ever having to log in to Monte Carlo. The constant monitoring of data freshness, anomalies are key to proactively identifying issues before they cause downstream issues. Also, the collaboration with the product team at Monte Carlo has made implementing this tool painless. They are quick to respond and always open to UI suggestions and improvements.
What do you dislike about the product?
Minor UI details such as sorting & searching ability on some pages.
What problems is the product solving and how is that benefiting you?
Anomaly detection in our data pipelines. Data freshness of tables.
Solid product with a lot of benefits!
What do you like best about the product?
Montecarlo is excellent at being an "always on" solution, continually monitoring our data warehouse, and alerting us to any issues that are coming up. One of the best ways it does this is through a Slack integration that is really easy to monitor and most of the time, makes it unnecessary to even log into MonteCarlo. MonteCarlo also makes it really easy to trace the lineage of upstream and downstream tables, which eases the process of troubleshooting which upstream data might be causing a failure, and also show which downstream datasets might be impacted
What do you dislike about the product?
Montecarlo doesn't support the end to end engineering pipeline. It would be great to see them add functionality to set up connections with Datadog and similar platforms so that it is easier to understand interdependencies of failures. Eg. How do we ensure that both a data analyst or product manager are not trying to troubleshoot a problem when an engineer is also doing the same?
What problems is the product solving and how is that benefiting you?
We've been trying to gain more visibility into our data and see where problems are arising, without building out a full DQ/monitoring solution. It's been really easy to find issues and work to address them with MonteCarlo. There's less guessing where the error is, and in most cases, it is alerting us that table X hasn't been refreshed, or a lot of rows were deleted from table Y etc.
Great product, great service!
What do you like best about the product?
Monte Carlo is super easy to implement and use, it's basically a "plug & play" product which doesn't require almost any set up from the client's side.
The product itself is great and really helped us address some serious data reliability / observability pain points in our data pipelines.
Their service is great, they are super responsive, always willing to hear new ideas and answer questions in no-time.
The product itself is great and really helped us address some serious data reliability / observability pain points in our data pipelines.
Their service is great, they are super responsive, always willing to hear new ideas and answer questions in no-time.
What do you dislike about the product?
Nothing I can think of, it's really a great product!
What problems is the product solving and how is that benefiting you?
We use MC mainly for 2 things:
1) Detecting anomalies in out data pipelines.
2) Getting better visibility into our data lineage (which fields/tables are connected to which dashboards).
With MC we managed to address both needs in one tool.
1) Detecting anomalies in out data pipelines.
2) Getting better visibility into our data lineage (which fields/tables are connected to which dashboards).
With MC we managed to address both needs in one tool.
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.
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.
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