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Monte Carlo is the perfect companion for Analytics Engineers
What do you like best about the product?
The UI is almost perfect, and also the backend: incidents, catalog, lineage. All the content is really good. It helps a lot when you are debugging issues, and it has been a great tool to train and onboard others. The support team is great, and I love that.
What do you dislike about the product?
There's not a Jira integration yet, and it makes tracking a bit harder. However, there's some features that you can use to keep incidents and tickets tidy, you can use comments and also the API (and build reports on your own).
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us identify and track incidents of different types: volume, field health,freshness, schema changes, custom validations and others. It alerts you when there are issues, and helps you with some details and hints about the issue itself, and the related items (that is: tables, views, looker views, Looks and Dashboards).
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Data observability without the hassle of rolling your own solution
What do you like best about the product?
I used to work for a company where we had a WHOLE TEAM dedicated to establishing observability on the datasets we built and maintained. The solution consisted of ad-hoc queries and home-grown tooling.
Enter Monte Carlo, which is a revelation in many ways. Our team likes that Monte Carlo makes establishing data observability a matter of configuration as opposed to stitching together disparate systems and queries to make it happen. The feature that ties it all together is the granular lineage functionality, which allows us to understand the health of our data and most importantly, understand who/what are impacted when something goes wrong upstream in our data stack.
The Product and Customer Success group are, in my opinion, the key to why we have had such a great experience with Monte Carlo. They've been so helpful in keeping us engaged, teaching us best practices, and receiving product input that actually gets implemented as new features!
Enter Monte Carlo, which is a revelation in many ways. Our team likes that Monte Carlo makes establishing data observability a matter of configuration as opposed to stitching together disparate systems and queries to make it happen. The feature that ties it all together is the granular lineage functionality, which allows us to understand the health of our data and most importantly, understand who/what are impacted when something goes wrong upstream in our data stack.
The Product and Customer Success group are, in my opinion, the key to why we have had such a great experience with Monte Carlo. They've been so helpful in keeping us engaged, teaching us best practices, and receiving product input that actually gets implemented as new features!
What do you dislike about the product?
I think the Monte Carlo UI could use a refresh. It's not the prettiest, although we care much more about functionality over looks. We're also hoping for more programmatic ways to control Monte Carlo in terms of how we configure observability. The point and click functionality of Monte Carlo is great for broader data observability considerations, but there are times where we'd like to make multiple config changes and save the clicks at the same time (Good example: turning off monitoring for multiple tables).
What problems is the product solving and how is that benefiting you?
Understanding the health of our data is crucial to everyday decision making using the data that our stakeholders rely on. Understanding the health of our data helps us to ensure data quality.
Monte Carlo Review
What do you like best about the product?
Ease of setting up a POC with clearly defined objectives and KPIs. Very quick onboarding once we moved forward with implementation and immediate, out-of-the-box features such as taps into Snowflake and Looker. Monthly touch points with the Customer Success team to ensure the product is being utilized properly and exposure to various features available within Monte Carlo.
What do you dislike about the product?
No real negatives to mention. The Monte Carlo team did a great job in facilitating integration.
What problems is the product solving and how is that benefiting you?
We now have an easy way to trace lineage of our data where we can understand how objects in DB tie into our Dashboards in Looker. We also get observability with freshness and schema monitoring coming into our Slack channel where we have a set of eyes on the alerts.
Monte Carlo's Data Reliability tool is really helping us build trust around data.
What do you like best about the product?
Automatic field health monitoring is a great feature, we can detect issues we wouldn't be aware of without Monte Carlo.
Monitoring volume spikes and drops also helps us identify a lot of "human invisible" issues.
Detecting freshness issues is also really useful for data engineers and data analysts to avoid data downtime to end users.
We managed to scale business rules testing by connecting Monte Carlo SQL custom monitors directly to Slack, to the right people.
Overall, fast and great results after using the tool for only 3 months.
Monitoring volume spikes and drops also helps us identify a lot of "human invisible" issues.
Detecting freshness issues is also really useful for data engineers and data analysts to avoid data downtime to end users.
We managed to scale business rules testing by connecting Monte Carlo SQL custom monitors directly to Slack, to the right people.
Overall, fast and great results after using the tool for only 3 months.
What do you dislike about the product?
UX is not adapted to non-tech users yet in my perspective, that could be a possible improvement for the future, as Business Analysts might also become heavy users of this tool.
What problems is the product solving and how is that benefiting you?
1. Monitoring data
2. Connecting data issue alerts to appropriate data owners
3. Finding issues that are "invisible" to the human eye, or issues that would take too much effort to an analyst to detect
2. Connecting data issue alerts to appropriate data owners
3. Finding issues that are "invisible" to the human eye, or issues that would take too much effort to an analyst to detect
Observability With a Lot of Potential
What do you like best about the product?
Being able to see data assets and the data flowing through them is super valuable for any team requiring high-quality data operations. The team supporting it is top-notch, they really care about hearing feedback and improving their product. That customer focus is also evident in the feature velocity, we've been impressed with new features and UI updates they have released recently.
What do you dislike about the product?
There could be more integrations with other tools in the modern data stack. The BI lineage could be improved with Looker. The data discovery experience could be improved as well.
What problems is the product solving and how is that benefiting you?
Ensuring stakeholders have the right data at the right time is crucial for us to respond quickly and meet our business goals. Lineage and out-of-the-box alerting are critical here.
One-Click Data Quality and Lineage
What do you like best about the product?
Monte Carlo took all the work out of monitoring our data warehouse for data lineage and quality. We have a small internal data team and being able to set up Monte Carlo and immediately enable huge capabilities we couldn't even imagine with a team of our size was truly incredible. It's one of the best data products I've ever paid for. Getting alerts in different slack channels, setting up different data domains, and the dbt integrations are also next-level features we didn't expect to love, but we do! 10/10 would buy again.
What do you dislike about the product?
Honestly, I don't have really anything to say here. Given the capabilities and insights we get from Monte Carlo, it's definitely worth the price. It's been huge for us.
What problems is the product solving and how is that benefiting you?
Monte Carlo delivers us data quality and data lineage for our whole data stack. It saved our small team months and months of work. It also delivers "unknown unknown" insights via machine learning -- a feature we would never have built internally.
The ultimate data observability in your data stack!
What do you like best about the product?
- Ability to set up alerts easily
- Variety of alerting
- Integration with Slack
- Data lineage!
- Variety of alerting
- Integration with Slack
- Data lineage!
What do you dislike about the product?
If I had to nitpick,
- more api features or monitors-as-code
- ability to view multiple field lineages together
- more api features or monitors-as-code
- ability to view multiple field lineages together
What problems is the product solving and how is that benefiting you?
- Helping with data monitoring alerts on new models
- Mapping out field lineage since we have data models across different systems
- Mapping out field lineage since we have data models across different systems
Everything we needed
What do you like best about the product?
The variety of ML automatic detection rules. The fact that you can write your own rules with code, fits like a glove with the CI/CD workflow that we use in our company.
What do you dislike about the product?
The lack of error reporting when you initially set up your filters by code. When you install them you don't know if they are incorrect until they are run, usually 2 days from the day of setup.
What problems is the product solving and how is that benefiting you?
Data quality is the main issue that we have with our data sets. Most of our data come from NoSQL store that then gets normalized into a data warehouse. Data warehouses do not enforce constraints as transactional databases so with Monte Carlo we can locate issues that we couldn't before.
A Truly Game-Changing Platform for Our Day-to-Day
What do you like best about the product?
Many SaaS platforms advertise minimal setup or work required of the client to activate and use their product. Monte Carlo truly delivers on that. There was very little setup work required for us, and once Monte Carlo's machine learning algorithms learned the data in our warehouse, we were getting value right away. It was exposing issues we never would have noticed on our own.
What do you dislike about the product?
Documentation can be a little thin at times, but thanks to how intuitive most of Monte Carlo's features are, there's rarely a need to reference the documentation.
What problems is the product solving and how is that benefiting you?
Monte Carlo exposes overall data quality issues across our warehouse. Be they late delivery of data, anomalous changes in table sizes, large shifts in low-cardinality columns, or any number of other problems caused by source system inconsistencies or breaking changes inadvertently deployed by engineers. The notifications platform additionally allows data practitioners at JetBlue to be alerted to issues in tables they commonly use at the same time our engineering team is alerted to the issue, eliminating the need for our engineering team to send out ad-hoc communications to analysts to make them aware of an issue while they work to solve it.
Have a database? Get Monte Carlo
What do you like best about the product?
I was constantly lost in a sea of 2,800 tables without any documentation. Every relationship and quirk was handed down through the generations like an arcane oral history of our business structure. Some tables were unknowingly deprecated, others had fields constantly in flux, and countless others were for testing purposes only. Monte Carlo won't write your business documentation for you but it WILL give you a map and a compass. I can now see every change that developers make to source tables: data types, new fields, dropped fields, mass insertions/deletions, everything. And it doesn't just tell you that a change occurred - it shows you the actual statement that was executed, when it was executed, and which downstream reports and users will be affected. Maybe you're not a developer and you don't care about the database structure - maybe you care about the timbre and characteristics of the data itself. Monte Carlo will show you that invoices are coming in with $0, or when users stop uploading data, or when the data you're receiving doesn't seem right. You can certainly try to write scheduled queries for all of these business alerts but you won't do it as well - or as quickly - as Monte Carlo. I didn't know that I needed Monte Carlo because I didn't know how to put my daily frustrations into words. I feel like I've gone to data therapy and I can finally keep up with the sprawling growth of our data structures.
What do you dislike about the product?
If I had Monte Carlo throughout my career in IT and analytics I wouldn't be near as good at troubleshooting system issues. If you've endured the hardships and learned how to find root causes already then jump right in. People should learn how to piece the data together by hand, document processes, and write automated alerts, THEN graduate to Monte Carlo to make their life easier.
What problems is the product solving and how is that benefiting you?
Just yesterday I was able to identify and clean up 150 tables that weren't being read or written to. I also use the catalog to track down all the tables that have a specific field I need for reporting. When things break, Monte Carlo identifies the means, motive, and opportunity of the culprit.
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