Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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The future of data observability
What do you like best about the product?
With the growing complexity of data pipelines, MC helps navigate the data flow to and from our databases.
It is, in many cases, the first tool I would use to identify the source of a specific data piece (even up to a single column).
Every new feature or update seems to solve problems I didn't even think about and is always helpful in pinpointing the origin of issues.
Customer success is super helpfull and proactive in any engagment.
Setting up monitors on tables is done with a simple press of a button and it is very easy to follow up on alert status.
I can't imagine going back to a data environment without MC.
It is, in many cases, the first tool I would use to identify the source of a specific data piece (even up to a single column).
Every new feature or update seems to solve problems I didn't even think about and is always helpful in pinpointing the origin of issues.
Customer success is super helpfull and proactive in any engagment.
Setting up monitors on tables is done with a simple press of a button and it is very easy to follow up on alert status.
I can't imagine going back to a data environment without MC.
What do you dislike about the product?
On rare occasions, there are some minor UI issues, but those are resolved very quickly.
Aside from that, nothing much bothers me.
Aside from that, nothing much bothers me.
What problems is the product solving and how is that benefiting you?
With MC's plug-and-play monitoring, we gained the ability to detect issues across the entire warehouse without having to set up alerts manually for every single table.
Also, having the ability to pinpoint the cause of an issue with just a few simple steps saves a ton of time and effort.
It further helps when non-engineers can now explore the pipelines without the help of the person who set the pipeline up.
Also, having the ability to pinpoint the cause of an issue with just a few simple steps saves a ton of time and effort.
It further helps when non-engineers can now explore the pipelines without the help of the person who set the pipeline up.
Monte Carlo is helping our team build trust in the quality of our data
What do you like best about the product?
Monte Carlo's out-of-the-box anomaly detection saved our team an immense amount of time and helped us catch data quality issues before they became reporting issues. That has helped our entire team feel more confident in our data.
What do you dislike about the product?
Our team would like to be able to customize the messages and the routing of the alerts with a little more nuance and context. The MC team has already implemented multiple features to try to help with this so I am confident that in a few months this will no longer be a gap.
What problems is the product solving and how is that benefiting you?
Our team is trying to reduce distrust in our data systems. That distrust manifests itself in ad-hoc requests to our engineering teams or bug-fix requests asking just to track down bad data at the source. Having end-to-end observability of our data has given our team the ability to monitor the whole pipeline so they can systematically build trust in our systems.
An immediate leap forward in data observability
What do you like best about the product?
The out-of-the-box anomaly detection on freshness and volume provides immediate value. The Slack notifications are very understandable and provide an easy way of linking back to specific incidents in the Monte Carlo UI. Additionally, the lineage capabilities are very useful when identifying the downstream impacts of any incidents. Monte Carlo has also been extremely receptive to feedback and provided timely suggestions and updates as appropriate. Overall the product impact has been extremely positive, and it has been a pleasure working with their team.
What do you dislike about the product?
The routing of notifications based on different types of alerts is not as granular as would be ideal, but they have been receptive to that feedback and have indicated there will be improvements coming in that area.
What problems is the product solving and how is that benefiting you?
The #1 problem that we are solving is timely awareness of any data issues; the combination of automated anomaly detection, customized rules and notification routing provides an effective way of configuring various alerts. An additional benefit is the data lineage visibility - this was not our primary reason for implementing Monte Carlo but has been very useful in tracing the impact of incidents.
MC really helps your data quality
What do you like best about the product?
- it monitors and alerts on all your data warehouse tables out-of-the-box
- transparent and diligent customer success and engineering teams
- easy to incorporate into your day-to-day operations (especially if you use Slack alerts)
- Monte Carlo has caught many critical issues before customers noticed (on top of uncovering silent data issues in our data warehouse)
- transparent and diligent customer success and engineering teams
- easy to incorporate into your day-to-day operations (especially if you use Slack alerts)
- Monte Carlo has caught many critical issues before customers noticed (on top of uncovering silent data issues in our data warehouse)
What do you dislike about the product?
Monte Carlo is always accepting feedback from their clients and actively improving their products. If I list a few minor UI issues today, the Monte Carlo team will probably resolve these by the time you are reading this. I'd recommend being open and sharing your feedback directly with the Monte Carlo teams.
What problems is the product solving and how is that benefiting you?
We had a large data warehouse with limited data observability (we only had data validations and alerts for a selected few critical pipelines). By adding in Monte Carlo, we gained data observability on all other data sets without sacrificing months of data engineering development work. Monte Carlo has helped improve trust in our data and pass on a sense of data ownership to data consumers and producers.
Great Out of the Box Functionality with Really Bright Future
What do you like best about the product?
- The ML powered anomoly detection provide great low effort reliability checks.
- The lineage feature is great building block mapping data lifecycle and determining impact.
- Incident IQ page provides an really helpful interface for working data incidents and tracking progress.
- Slack integration allows for quick triage and resolution use cases.
- The customer success team is super accessible and a pleasure to work with.
- The entire team from CEO down is really helpful and super interested in partnering with customers to make the product better.
- The GraphQL API is really easy to use and provides a great starting point for extending the product.
- The lineage feature is great building block mapping data lifecycle and determining impact.
- Incident IQ page provides an really helpful interface for working data incidents and tracking progress.
- Slack integration allows for quick triage and resolution use cases.
- The customer success team is super accessible and a pleasure to work with.
- The entire team from CEO down is really helpful and super interested in partnering with customers to make the product better.
- The GraphQL API is really easy to use and provides a great starting point for extending the product.
What do you dislike about the product?
- I would like to see more automation around the Incident IQ feature to be on par with other incident management tools like Datadog, Pagertree, and Rootly.
- More first-class support for dbt, Prefect/Airflow, Fivetran, Kafka. However, adding these via the lineage API is possible and something we do.
- SDK/CLI for creating MC objects/monitors in code. However, building this internally via the monitors API is possible.
- Catalog feature needs some updating to be on par with companies focused on that feature.
- More first-class support for dbt, Prefect/Airflow, Fivetran, Kafka. However, adding these via the lineage API is possible and something we do.
- SDK/CLI for creating MC objects/monitors in code. However, building this internally via the monitors API is possible.
- Catalog feature needs some updating to be on par with companies focused on that feature.
What problems is the product solving and how is that benefiting you?
- Automated data quality monitoring and notifications
- Data lineage to troubleshoot issues, plan changes, and document full data lifecycle
- Snowflake variant schema change monitors
- Data incident management
- Custom monitors as needed
- Data lineage to troubleshoot issues, plan changes, and document full data lifecycle
- Snowflake variant schema change monitors
- Data incident management
- Custom monitors as needed
Great Data Observability Product and Great Support Service
What do you like best about the product?
Easy to set up just plug and play;
Lineage tracking for upstream and downstream dependencies;
Data freshness monitoring;
Great service, very responsive always open for product improvement suggestions;
Lineage tracking for upstream and downstream dependencies;
Data freshness monitoring;
Great service, very responsive always open for product improvement suggestions;
What do you dislike about the product?
Nothing I could think of, I really like the product and want to spend more time with the product to build some custom data context validation rules.
What problems is the product solving and how is that benefiting you?
Data quality monitoring and alerting, improving data governance
Recommendations to others considering the product:
Don't hesitate start using the product right away you will be pleasantly surprised
Great out of the box functionality
What do you like best about the product?
Custom alerts allow users complete flexibility in what they would like to monitor. Their integration with Slack allows us to know when a problem occurs without checking the dashboard constantly. The product team actually listens to feedback and takes action. We've seen feature enhancements that come directly from our request.
What do you dislike about the product?
None that I can think of at the moment. Solid product and a solid team.
What problems is the product solving and how is that benefiting you?
The data org is now able to be active (instead of reactive) and resolve any data anomalies that need to be addressed quickly and easily.
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.
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