Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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MC gained us visibility to our data and made it easier for us to communicate issues with our users
What do you like best about the product?
Easy UI, easy integration with other tools like slack/DBT/Tableau. Customer reps are also great- whenever we have a question, I know who to ask and I can reach out to the team directly
What do you dislike about the product?
Nothing much- there are a few features that we would like having, but I have flagged these to the product team and it seems like they are looking into it
What problems is the product solving and how is that benefiting you?
I am being alerted whenever my data breaks and it is easy to spot the impact of the data issue. Also, communicating the issues with our users is easy- they are part of the slack channel and are being alerted on time
MonteCarlo Usage Survey - WP Engine
What do you like best about the product?
The automated monitors that just work out of the box. While we leverage custom monitors quite a bit, having a set of automatically enabled monitors and alerts helps us move fast. MC has also been very easy to integrate into our existing stack. We also use the lineage feature quite a bit whiich measn we are in the platform daily. I also like the constant evolution of the UI and how the improvements always seem to be directed at the most valudable problems.
What do you dislike about the product?
There was a hicup in how we managed the transition to the new table model. Hats off you all that you worked with us to find a way to make it work. To offer a suggestion, having things like the usage report available before the decision was made would have been helpful. We had to go through a couple of iterations before we knew for certain what to look at to see our usage against our contract.
What problems is the product solving and how is that benefiting you?
We had a lagay data warehouse that was managed and maintained in silos by multiple teams. The primary use for MC was to offer out of the box solutiosn to both observe, identify and respond to data issues in a proactive way.
We are working with MonteCarlo to monitor the data health of our DWH platform.
What do you like best about the product?
Simple integration with data platform stack (in our case Snowflake,DBT,Looker)
Easy to use, no advanced technical skills required
Ability to monitor schema changes
Great catalog abilities (Lineage,search)
A single view of all data issues across the entire pipeline (DB to Looker)
Customer success and support are very responsive
Integration with Slack
Easy to use, no advanced technical skills required
Ability to monitor schema changes
Great catalog abilities (Lineage,search)
A single view of all data issues across the entire pipeline (DB to Looker)
Customer success and support are very responsive
Integration with Slack
What do you dislike about the product?
There is no option to save data errors in the database (only statistics and sample data can be obtained).
What problems is the product solving and how is that benefiting you?
Finding data issues in the DWH before they affect business users
impact analysis of data issues (using end to end lineage)
Monitoring DBT jobs
Schema changes (of raw data) alerts
impact analysis of data issues (using end to end lineage)
Monitoring DBT jobs
Schema changes (of raw data) alerts
MC - easy to use tool for monitoring data quality
What do you like best about the product?
Easy to use as a day to day monitoring tool. Great customer support, very responsive and always adding effective features.
What do you dislike about the product?
It does not easily output all erronous rows, the default is a sample.
There is no easy connection between one incident to the other.
There is no easy connection between one incident to the other.
What problems is the product solving and how is that benefiting you?
Our main issue is a daily feed of data from our partners which has a ton of errors. We set many monitors in MC, which alert us each day on new data incidents.
Devops Engineer Experience with Monte Carlo
What do you like best about the product?
It was very easy to integrate with AWS. Had very great experience with implementation team. I would like to emphasize on quick responses from customer support whenever we has questions or encountered issues.
What do you dislike about the product?
From integration and implementation point I had no issues but it would be great if redshift connectivity can be made through a role instead of local users
What problems is the product solving and how is that benefiting you?
Data Quality has improved
Data Observability with Monte Carlo is at a Whole New Level of Excellence
What do you like best about the product?
Monte Carlo has greatly enhanced our data observability capabilities.
It is a user-friendly platform, intuitive, and straightforward to integrate into our existing systems.
We've found their customer support to be commendable - quick to respond, helpful, and accommodating.
What stands out the most is the commitment to continuous improvement. The team regularly innovates and refines the product, keeping us engaged and optimistic about future enhancements.
It is a user-friendly platform, intuitive, and straightforward to integrate into our existing systems.
We've found their customer support to be commendable - quick to respond, helpful, and accommodating.
What stands out the most is the commitment to continuous improvement. The team regularly innovates and refines the product, keeping us engaged and optimistic about future enhancements.
What do you dislike about the product?
Despite the many positives, there are areas where improvement would be beneficial. Monte Carlo's integration with external tools such as PagerDuty and Opsgenie could be refined to add more functionality, it currently lacks some important features. Moreover, reducing noise in a large data lake could be easier, as it's quite a task at present.
What problems is the product solving and how is that benefiting you?
Monte Carlo enhances our data quality and observability, boosting transparency over our myriad of data assets. It alerts us to anomalies, allowing quick action, reducing troubleshooting time, and thus increasing our data's value.
My experience of Monte Carlo product and its team.
What do you like best about the product?
The best part of Monte Carlo is its UI and UX, it's designed very well so that a new user can navigate and understand the tool quickly.
The tools AI engine which gets trained over the metadata of our Bigquery is also a proven product, its detection of anomalies and raising incidents on its own as well as the alert fatigue management is industry-leading and other Goverance products are playing catchup.
The tools AI engine which gets trained over the metadata of our Bigquery is also a proven product, its detection of anomalies and raising incidents on its own as well as the alert fatigue management is industry-leading and other Goverance products are playing catchup.
What do you dislike about the product?
So far after a year of using the product and onboarding all Business Departments and receiving their feedback, I do not have any reason to dislike the product.
What problems is the product solving and how is that benefiting you?
Below are the problems that the product is solving:
1. Immediate automatic detection of fault or anomaly in data.
2. Data Lineage up to the BI Layer, in our case Looker; with additional integration points with DBT and Airflow pipelines.
3. Customization capability for extending lineage for cases when the sources are coming from diverse systems such as message queues, HTTP Post, and no Governance tool in the market was able to integrate and detect automatically when I was studying other tools and their customization endpoints came with limitations and hurdles.
4. Data Pipeline error detection for DBT/Airflow.
5. Snooze for alerts, reduces alert fatigue.
6. Automatic incident creation and incident management.
7. Incident Observability.
8. Asset catalog and ability to build Data Dictionary.
9. Enables homogeneous KPIs and Data Definition.
10. Administration of the Governance Tools.
1. Immediate automatic detection of fault or anomaly in data.
2. Data Lineage up to the BI Layer, in our case Looker; with additional integration points with DBT and Airflow pipelines.
3. Customization capability for extending lineage for cases when the sources are coming from diverse systems such as message queues, HTTP Post, and no Governance tool in the market was able to integrate and detect automatically when I was studying other tools and their customization endpoints came with limitations and hurdles.
4. Data Pipeline error detection for DBT/Airflow.
5. Snooze for alerts, reduces alert fatigue.
6. Automatic incident creation and incident management.
7. Incident Observability.
8. Asset catalog and ability to build Data Dictionary.
9. Enables homogeneous KPIs and Data Definition.
10. Administration of the Governance Tools.
Very helpful for our daily data monitoring
What do you like best about the product?
easy UI to use , integrating with other software such as Slack, easy
Good staff and costumer supports
Good staff and costumer supports
What do you dislike about the product?
The 500 breached rows returned maximum in the alert
What problems is the product solving and how is that benefiting you?
Monitoring daily data errors
Great software and team!
What do you like best about the product?
Monte Carlo has fundamentally changed how we triage data issues within our environment. It presents an easy-to-use software that gives you a single place to see the highlights on your data issue when an issue arrises. The support from the Monte Carlo team has been a real help too! I've regularly connected with thier internal teams (engineering, support, product) both to get assistance or to influence the roadmap. The MC team was able to deliver new features for me within 3--6 months of implementation. The documentation to get the integrations setup was very helpful and accurate. Monte Carlo is also delivering new features and wizards to make the setup process easier. We typically use the software a few days a week when it alerts us to issues or we are triaging something that came up from the insights team.
What do you dislike about the product?
Monte Carlo can be intimidating to setup and get configured with your data system. There are also many ways to setup monitors which requires time to review and adjust the results. We have had a few data issues appear that were not caught by the automated monitors, so I would recommend having custom monitors on critical tables and not just relying on the out-of-the-box, AI-based monitors.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us get a better understanding of what is happening to our data and alerts us when out of the ordinary trends appear.
Great tool for data reliability
What do you like best about the product?
Anomaly detection, key asset clarification, and lineage build.
We have a large number of ETL jobs but we don't have a good tool to govern those jobs until we have Monte Carlo. MC allows us to monitor thousands of our tables in the data warehouse.
We have a large number of ETL jobs but we don't have a good tool to govern those jobs until we have Monte Carlo. MC allows us to monitor thousands of our tables in the data warehouse.
What do you dislike about the product?
Recently we have some false alerts about the deleted tables, but support team helps us fix them quickly
What problems is the product solving and how is that benefiting you?
Anomaly detection, key asset clarification, and lineage build.
We have a large number of ETL jobs but we don't have a good tool to govern those jobs until we have Monte Carlo. MC allows us to monitor thousands of our tables in the data warehouse.
We have a large number of ETL jobs but we don't have a good tool to govern those jobs until we have Monte Carlo. MC allows us to monitor thousands of our tables in the data warehouse.
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