Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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My team stopped firefighting and started building again
What do you like best about the product?
The automated lineage and alerting are really important for our team. It’s a huge relief to know we’ll be the first to find out about bad data or a schema change rather than getting a message from a stakeholder downstream.
What do you dislike about the product?
There is definitely a bit of a learning curve to understanding the more advanced custom SQL monitors. While the UI is generally intuitive, getting the most out of the deeper observability features takes a significant time investment
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us tackle data downtime by catching silent failures like schema changes or stale tables before they impact our executive dashboards. It has completely changed our workflow from being reactive and firefighting to proactively fixing issues before the business even notices something is wrong. This is gamechanger for us, helping us keep the business happy.
Integrative Dashboards with Smooth Setup
What do you like best about the product?
I like Monte Carlo's integrations with SaaS products, especially with Databricks and Snowflake, which help us organize, predict, and respond effectively. The initial setup is good and straightforward.
What do you dislike about the product?
I find user management in Monte Carlo could be improved.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps with governance and organizes, predicts, and responds effectively by integrating with SaaS products like Databricks and Snowflake.
Clear, Actionable Alerts That Catch Data Issues Early
What do you like best about the product?
What I like best about Monte Carlo is how good it is about catching data issues before they become real problems. The alerts are clear and actionable, which saves a lot of time. It’s given us much more confidence in the reliability of our dashboards and reports.
What do you dislike about the product?
I’d like to see deep-level support for Spark on Databricks, when it comes to capturing column-level lineage for some of our more complex transformation jobs. While the high-level lineage is good, getting that granular detail sometimes requires more manual configuration than I’d prefer for a tool.
What problems is the product solving and how is that benefiting you?
It solves the problem of unreliable data and the fire drills that come with broken dashboards or failed pipelines. Instead of reacting to issues after stakeholders notice them, we can proactively detect and address anomalies early, helping us deliver business critical dashboards more smoothly.
Automated Data Lineage That Visualizes Pipeline Impact Instantly
What do you like best about the product?
The best part is the automated data lineage and how it visualizes the impact of a broken pipeline before the stakeholders notice. Instead of manually tracing dependencies in complex SQL or Spark jobs that we currently have, I can instantly see which dashboards will be affected by a schema change or a stale table.
What do you dislike about the product?
The initial setup for custom monitors is tricky, especially when i tried to tune SQL rules for business logic. While the ML alerts are great, getting the manual thresholds just right without triggering took some trial and error.
What problems is the product solving and how is that benefiting you?
It is solving the problem of data downtime. Those periods where data is missing, inaccurate, or broken without anyone knowing until it's too late. It provides a safety net by monitoring for freshness, volume shifts, and schema changes across our entire stack, which has been a blessing for me
Makes Monitoring Our GCP Pipelines So Much Easier
What do you like best about the product?
The way Monte Carlo surfaces anomalies in data freshness and pipeline behaviour is extremely helpful. It lets our team catch quality issues before they impact downstream users. The custom SQL query alerts are very accurate, and they save me a lot of time by pointing me straight to where things are breaking.
What do you dislike about the product?
The email alert formatting is restrictive — it’s difficult to insert clean tables or richer layouts for downstream users. More Outlook‑style formatting support would be a big improvement
What problems is the product solving and how is that benefiting you?
For me, the biggest value is the strong integration with Google Cloud. Monte Carlo picks up on freshness and pipeline issues across our GCP stack without any extra overhead. The custom SQL alerts are also a huge benefit — they let me monitor exactly what matters for our engineering datasets and surface issues in a very targeted way. Together, these help me identify problems early and keep downstream users informed
Powerful Monitoring, Complex Setup
What do you like best about the product?
I really appreciate the monitoring feature in Monte Carlo. It's great because we can write custom alerts and emails that are integrated with Teams, making it really easy to keep our stakeholders informed about any data quality issues or key updates they're looking for. It's really powerful for understanding exceptions in the data, even those that aren't directly failures or major data quality issues, which our team finds very valuable.
What do you dislike about the product?
It would be great to integrate the alerts and monitoring section more closely. Some of the UI elements could do with improvements. The standard parts in the emails could be adjusted since they always indicate pipeline failure or warning, but sometimes they are just informational. I also wish it could be integrated closer to our data to avoid repeating the same code in various places.
What problems is the product solving and how is that benefiting you?
I use Monte Carlo to expose DBT warnings and monitor trends over time, create custom rules for data alerts, and inform stakeholders of data quality issues through Teams integration.
Robust Data Quality with Some SQL Limitations
What do you like best about the product?
I like the ML-based anomaly detection and the ease of setting up data quality monitors in Monte Carlo. The web hook integration and data lineage features are valuable, especially for helping my data operations team troubleshoot issues by digging through data discrepancies. The process of setting it up was fairly straightforward.
What do you dislike about the product?
Column lineage is a bit limited with complex SQL and can be improved. An example is if there is a switch case where source data could be sourced based on condition, it is not yet supported.
What problems is the product solving and how is that benefiting you?
I use Monte Carlo for data observability and governance. It solves data quality, validation, and anomaly detection issues. The ML-based anomaly detection helps find unexpected data volumes, and data lineage aids in troubleshooting discrepancies by tracing data through its lifecycle.
Intuitive UI That Catches Issues Before They Hit the Pipeline
What do you like best about the product?
I really enjoy the intuitive UI. I also like that it helps catch issues early, before they make their way into the pipeline, which makes the overall process feel smoother.
What do you dislike about the product?
I do wish Monte Carlo were more “set and forget.” In the early phase, acknowledging incidents can take a while, especially with the number of monitors we’ve set up. I also wish there were a cooldown period after setting up a monitor in Monte Carlo, so the training data could keep learning until it’s truly “ready.”
What problems is the product solving and how is that benefiting you?
Identifying issues before it occurs. Seeing where the issue falls and speeding up my investigations help save my time.
Powerful what-if probability modeling, but results hinge on getting input distributions right
What do you like best about the product?
it transforms "what-if" scenarios into data-driven probability distributions, providing much more clarity than a single-point estimate ever could.
What do you dislike about the product?
The model is only as good as the probability distributions you feed it. If you choose the wrong input distribution (e.g., assuming a Normal distribution when the data is skewed), the results will be confidently misleading.
What problems is the product solving and how is that benefiting you?
I haven’t been using this as much
Automated Lineage and No-Code Monitors That Save Us Tons of Time
What do you like best about the product?
What I appreciate most is the automated lineage and the no code monitors that catch data quality issues before my stakeholders even notice. It saves me a ton of time on manual testing.
What do you dislike about the product?
I think the UI can get a little crowded when you’re managing a large number of tables, like how we do. The initial setup for custom monitors can sometimes feel a lot too.
What problems is the product solving and how is that benefiting you?
It has effectively eliminated the silent data failures that used to hurt our pipelines, specifically by catching schema changes and anomalies before they hit production.
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