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Monte Carlo has been a great observability tool
What do you like best about the product?
I like the customization of monitors and the ability to share that across various teams. Monte Carlo easily integrates with Slack, dbt, and Snowflake. Making our job of catching data quality issues a lot easier.
What do you dislike about the product?
There has been a little confusion on my part about how to set up customized notifications. I think this is part of being newer to the tool and not using all of the resources.
What problems is the product solving and how is that benefiting you?
Monte Carlo is helping us identify data quality issues before they reach stakeholders. There are automated tests that run, which we don't have to configure a breach point, Monte Carlo learns what is normal over a given period and lets us know when something looks off.
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Easy setting of alerts for monitoring data issues with SQL rulls
What do you like best about the product?
Easy to use, very efficient customer service and support, constantly improving and developing new features
What do you dislike about the product?
There are some features that would be helpful for us and still not available
What problems is the product solving and how is that benefiting you?
We use MC SQL rulls alerts to identify data issues. The alerting system is easy to use, and saves us time.
Monte Carlo increased accountability for our Engineering team
What do you like best about the product?
Visibility! Our Engineering team set up a system of alerts on the tables we care about that feed into multiple Slack channels that our Operations team members can watch and even comment on the incidents. Monte Carlo has improved the accountability within the Engineering team while providing higher quality of service to our business partners.
What do you dislike about the product?
-The table lineage images are very helpful. Any improvements to always see the full names would be appreciated.
-I would love to see more details surfaced for Tableau workbooks within Monte Carlo
-It would be great to immediately see a list of incidents open for you/your team upon log-in. What ends up happening for our teams is that incidents that need investigation or further research don't end up getting closed in Monte Carlo because its too tedious to go find them several hours or days later.
-The permissions roles are a little confusing. It seems (to me) they could be simplified into 3 groups: admin, editors, and viewers.
-I would love to see more details surfaced for Tableau workbooks within Monte Carlo
-It would be great to immediately see a list of incidents open for you/your team upon log-in. What ends up happening for our teams is that incidents that need investigation or further research don't end up getting closed in Monte Carlo because its too tedious to go find them several hours or days later.
-The permissions roles are a little confusing. It seems (to me) they could be simplified into 3 groups: admin, editors, and viewers.
What problems is the product solving and how is that benefiting you?
-It surfaces BigQuery log reporting, which is a skill we do not currently have in our engineering team
-Near real-time feedback on potential data issues for on-call Engineers to immediately dig into and resolve to keep lights-on and smooth reporting experience for the rest of the company & our clients
-We can easily see the lineage of content in our database (BigQuery) and connect it to reporting (in Tableau), which helps us identify and focus our attention on key assets and aids us when deprecating content
-Near real-time feedback on potential data issues for on-call Engineers to immediately dig into and resolve to keep lights-on and smooth reporting experience for the rest of the company & our clients
-We can easily see the lineage of content in our database (BigQuery) and connect it to reporting (in Tableau), which helps us identify and focus our attention on key assets and aids us when deprecating content
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.
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