Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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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.
Complete and intuitive
What do you like best about the product?
Very easy to set up and use, responsive development team, automates valuable testing and analysis
What do you dislike about the product?
Nothing I can think of, there have been a few UI changes lately which force you to reevaluate how you use it, but they are usually for the better and communication around it is great
What problems is the product solving and how is that benefiting you?
Database table usage, proactive analysis of schema and data quality/quantity changes, allows us to fix issues before it impacts end users
Awesome Data anamoly detection tool
What do you like best about the product?
They have a very good anomaly detection tool. The ML at the backend is superb and helps us identify any issue before business teams find it.
What do you dislike about the product?
Rules section of the tool is pretty cluttered and not user friendly
What problems is the product solving and how is that benefiting you?
Data anomaly. Early prediction of problems
Great data monitoring product!
What do you like best about the product?
The ability to see upstream and downstream dependencies of data tables. This makes troubleshooting much easier when a problem occurs. Slack integrations make it easy to monitor anomalies and data issues without ever having to log in to Monte Carlo. The constant monitoring of data freshness, anomalies are key to proactively identifying issues before they cause downstream issues. Also, the collaboration with the product team at Monte Carlo has made implementing this tool painless. They are quick to respond and always open to UI suggestions and improvements.
What do you dislike about the product?
Minor UI details such as sorting & searching ability on some pages.
What problems is the product solving and how is that benefiting you?
Anomaly detection in our data pipelines. Data freshness of tables.
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