Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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MC help my day to day both with identifying when data breaks and avoiding it.
What do you like best about the product?
MC integrates with all the stiff we work with. of course snowflake. but not only - dbt, Jira, PagerDuty and Github. This enables us expanding the product with the tools we anyway use for our day-to-day.
What do you dislike about the product?
MC in a way is a closed box. building custom workflows using it's api/cli is an easy task. It seems the approach is to build everyting for us, instead f providing us the tools and the data demystified for us to build workflows that suit us. for example, if we make a change in table X, MC has the lineage but only we know that a specific type of table in depth x should be changed and notified to team z. another example is thet MC is trying to build for us dashboards such as data reliability and performance dashboards. every data team has it’s nuances and it’ll be impossible to get it right for all (or most teams).
What problems is the product solving and how is that benefiting you?
automatic alerting is very powerful. the fact that we know that any new dataset is monitored by MC (even if it's basic monitoring) is very reassuring. 95% of "downtime issues" are caught by these automatic alerts. I beleive the data catalog is very pwoerful but unfortunately we as an org are not succeeding with adopting it.
A great tool to monitor your data quality
What do you like best about the product?
Monte Carlo is a great platform for us building our data quality checks and get ahead of the problems before our customers find them. Its built-in ML-based detections helped us identify potential data issues early (that upstream tasks failed to run).
What do you dislike about the product?
When we initially adopted the platform, it was missing several key features which got built quickly over the past year. Hence, it (kinda) remains a new stack and we are still making adjustments to utilize it more efficiently for our operations.
What problems is the product solving and how is that benefiting you?
It solves our data monitoring issue and notification issues. It allows us to quickly build and iterate monitoring contents. As a result, we can quickly test and migrate different monitoring strategies.
Not sure how we would live without it
What do you like best about the product?
One of my favourite things about Monte Carlo was how quickly we were able to integrate and get value from the tool. As a growing company too, Monte Carlo continues to rapidly add new features that benefit us, with a product team and customer support that are more than willing to garner our feedback and listen to our feature requests, many of which have been implemented.
What do you dislike about the product?
There is not much I dislike about Monte Carlo. The only thing that comes to mind is that sometimes their documentation is not the most clear and understandable.
What problems is the product solving and how is that benefiting you?
We use Monte Carlo to solve several areas for us; these include
* Data Lineage - I do not know how we lived without this! Not only is this beneficial in understanding sources of data to track through but helps us understand the blast radius of downstream impacts so that we can notify stakeholders.
* Ownership - as the number of data assets increases, having defined owners makes accountability transparent and brings operational excellence to the data world.
* Monitoring and Alerting - the ease with which owners of data assets can set up monitors and integrate alerting into their current workflow is excellent. The automatic and domain-defined alerts allow us to get up and running quickly, keep our data lake pristine, and avoid it becoming a swamp.
* Data Lineage - I do not know how we lived without this! Not only is this beneficial in understanding sources of data to track through but helps us understand the blast radius of downstream impacts so that we can notify stakeholders.
* Ownership - as the number of data assets increases, having defined owners makes accountability transparent and brings operational excellence to the data world.
* Monitoring and Alerting - the ease with which owners of data assets can set up monitors and integrate alerting into their current workflow is excellent. The automatic and domain-defined alerts allow us to get up and running quickly, keep our data lake pristine, and avoid it becoming a swamp.
The Ease of using Monte Carlo while developing
What do you like best about the product?
Monte Carlo has been great to work with. We started using monitors as code at our project's start, which was great. We asked for a few things to make the process smoother, like notification tuning and better integration with dbt. Monte Carlo took that advice and pivoted with their plan to implement it for us. They are constantly improving, and I am excited to continue to use the product. They are even adding features that would allow us to reduce the number of Monitors as code we have to maintain in our code.
What do you dislike about the product?
With monitors as code, I would like to have the ability to rename the rules without them resetting. As well as having the ability to reset the history of a monitor in more of an on-demand fashion. Our product is newer, and as such, the data is constantly transforming.
What problems is the product solving and how is that benefiting you?
Monte Carlo is what we use to help us insure the data is consistent. It is consistently delivered, in a consistent shape as well as ensuring no abnormalities in the way it is transformed.
An Invaluable Data Observability Tool
What do you like best about the product?
Love the user-friendly UI of this intelligent data observability tool. Monte Carlo has been super helpful in keeping a health check of our data assets. Once configured for your database, the algorithms monitor existing/future data assets for freshness, volume, and schema changes. If you need more, it allows you to set up custom monitors. A great tool overall!
What do you dislike about the product?
I like the tool so far, and hoping it will evolve!
What problems is the product solving and how is that benefiting you?
Allows us to identify the data issues well before consumers
Up and running with data observability across production.
What do you like best about the product?
Great POC process, impressed over competitor products with the smoothness of the POC. The easiest product to get up and running with basic observability checks. Has been making improvements to the product relatively quickly. The overall direction of the product aligns well with our needs and business direction. The team is a pleasure to work with, open to feedback, and willing to have critical discussions. Many of the recent improvements have cleared up our team's most urgent needs for the product. All of these improvements have happened within 6 months of our implementation.
What do you dislike about the product?
Could use some maturation in the onboarding process, more hands-on, technical, and industry-specific. There were many to-be-desired features when we first implemented. Many of those features have now been implemented, which is within six months of starting our implementation. More transparency into the product roadmap, what features are getting worked on, what some estimated release dates are, and what the highly rated/asked-for features are. Persistent UX/UI issues that could use improvement have not been fixed quickly.
What problems is the product solving and how is that benefiting you?
Monte Carlo has allowed us to implement observability on our entire product analytics stack. From ELT, to Data Lake, to transformations, to Data Warehouse, to Reporting/Dashboarding. We have also started to dive into deeper Data Quality monitoring as well. This allows our centralized data team to react faster, notice issues before the business does, route and notify the correct business and technical users about anomalies, and make sure our product analytics environment is running as reliabily as possible.
Using Monte Carlo for observability of data pipelines
What do you like best about the product?
Monte Carlo enables us to monitor our data pipelines and alerts us if we haven't received an expected data refresh from our external partners. For example, a downstream user was concerned that partner data was stale; we quickly confirmed that while the pipeline continued to refresh, new records were not being received. Sharing this information visually via Monte Carlo with our downstream users is also very effective. Monte Carlo provides alerts via Slack which we use to keep relevant users informed. This provides an additional level of transparency and data ownership for our business users.
What do you dislike about the product?
Because Monte Carlo provides such breadth and depth of information, it can take a bit to get up to speed on using the product. I have found their production tutorials helpful, but even more beneficial to have a session with your customer success manager. It's also essential to manage notifications in relevant channels so they don't become noise.
What problems is the product solving and how is that benefiting you?
We use the domain feature to alert business users to any upstream issues in source data. Often this may be a problem not with the data pipeline, but the data provider; understanding this allows the business stakeholder to take quick action. Custom monitors provide alerts on key data fields pinpointing missing or inaccurate data before it impacts downstream BI.
Invaluable tool and exceptional customer support
What do you like best about the product?
Monte Carlo is a gamechanger for our company and invaluable for our mission to improve data quality. The built in anomaly detection monitors are super helpful, and the custom monitors are perfect for alerting us to issues that are particular to our data sets. We rely heavily on the data lineage and impact radius analysis to figure out where data is coming from, going to and who is using it. I can't imagine running a data team without this tool.
What do you dislike about the product?
Monte Carlo is very close to having a fully functional data catalog that would obviate the need for another tool. I'd love to see that feature fully fleshed out.
What problems is the product solving and how is that benefiting you?
Monte Carlo is a key part of our data certification and governance program. We can't have confidence in our reports if we don't know about underlying data issues. Monte Carlo gives us deep insight into data quality, data anomalies and changes in the data that would otherwise take the team a long time to find, or worst case, our stakeholders discover when looking at a report.
Monte Carlo enables a quick start and deep insights into Data Reliability
What do you like best about the product?
Monte Carlo delivers a 'low water mark' in Data Reliability with little time investment. We are up to speed across our main BigQuery datasets in a matter of weeks and have enhanced our Incident Management and Root Cause Analysis abilities considerably. Monte Carlo is also well liked by the Engineers as it enables a high degree of automation
What do you dislike about the product?
We can limit which datasets are scanned - and their associated license cost - at the GCP Project and Dataset level, but ideally we'd like to be able to set this at the individual table level.
Not really a dislike, butwe would like support for Kafka streams as that would enable Observability even closer to the source.
Not really a dislike, butwe would like support for Kafka streams as that would enable Observability even closer to the source.
What problems is the product solving and how is that benefiting you?
We currently have a fragmented approach to Data Quality testing across the Organisation. Teams are responsible for their own quality, and those that do create monitoring do so using different platforms and tools and rarely publish metrics consistently. We are beginning to roll out Monte Carlo across all our Data Teams. Monte Carlo delivers a high degree of consistency across the organisation enabling us to get a pitcure of overall reliability and generate some metrics that will allow us to set targets to improve quality across our entire data estate.
In addition - and not initially a driver - Monte Carlo helps us with its incident management, ensuring that issues are owned and categorised effectively.
And a further benefit of Monte Carlo are the Insights into the tables its scanning - we've already identified objects that we didn't think were important, but are referenced multiple times in pipelines and the usage reports have helped us prune unused tables
I should also call out both Monte Carlo's pre-sales/ Proof of Concept support and Customer Onboarding - both of which have been excellent. Their customer onboarding in particular is probably the best I've encountered.
Monte Carlo is not cheap, but is worth the money!
In addition - and not initially a driver - Monte Carlo helps us with its incident management, ensuring that issues are owned and categorised effectively.
And a further benefit of Monte Carlo are the Insights into the tables its scanning - we've already identified objects that we didn't think were important, but are referenced multiple times in pipelines and the usage reports have helped us prune unused tables
I should also call out both Monte Carlo's pre-sales/ Proof of Concept support and Customer Onboarding - both of which have been excellent. Their customer onboarding in particular is probably the best I've encountered.
Monte Carlo is not cheap, but is worth the money!
The most essential piece of our Data Quality Management
What do you like best about the product?
I love that I'm able to not only receive notifications but generate reporting metrics that describe the state of data processes without manual work.
What do you dislike about the product?
For tables that are updated in a non-deterministic way, it's hard to configure alerting properly.
What problems is the product solving and how is that benefiting you?
We catch issues that we would not have known about
We can quickly troubleshoot root causes in data sets
We can report to the business on the state of our data processes honestly and accurately
We can quickly troubleshoot root causes in data sets
We can report to the business on the state of our data processes honestly and accurately
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