Monte Carlo works as a centralized data tool. We use it for data observability and anomaly detection, which helps identify issues and changes in data flows.
Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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Provides centralized data observability features and has an easy-to-use user interface.
What is our primary use case?
What is most valuable?
The product allows us to segment data into domains and categories. It makes organizing work easier based on its relevance to specific projects and teams. Additionally, the orchestration layer within the tool plays a vital role in streamlining data processes.
What needs improvement?
For anomaly detection, the product provides only the last three weeks of data, while some competitors can analyze a more extended data history. This feature needs improvement. Its price could be a bit competitive compared to competitors offering similar services.
For how long have I used the solution?
We have been using Monte Carlo for three years.
What do I think about the scalability of the solution?
The product is scalable. However, they could improve the pricing and provide more cost-effective ways of scaling.
How are customer service and support?
They offer efficient customer service. They provide essential documents, feedback, and solutions. They set up a meeting with us whenever we require more information.
How would you rate customer service and support?
Positive
How was the initial setup?
The product's initial setup is in a daily improvement stage, deploying new plugins for upstream and downstream resources. It takes 25 minutes to complete. The process involves integrating with third-party services for Single Sign-On (SSO). It requires only one executive for maintenance as it has easy-to-use navigation and user interface.
What's my experience with pricing, setup cost, and licensing?
The product has moderate pricing.
What other advice do I have?
The product has centralized nodes and is a pioneer in the data observability domain. It has helped a lot in investigating system issues. It also saves a lot of time in identifying issues by improving data traffic.
I rate Monte Carlo a nine out of ten.
Monte Carlo is complex
What do you like best about the product?
MC has been a great product for us for a while, allowing us to make complex SQL queries and automated queries providing alerts we need. These alerts help us know when things break and where along the pipeline we need to implement a fix.
What do you dislike about the product?
We have recently debated the benefits of Monte Carlo because there are other products we use that can cover the same category. These can include automated queries in Airflow or Hightouch. Also, MC's UI can be a bit too complex to navigate.
What problems is the product solving and how is that benefiting you?
MC is helping us keep track of our data and alerting us when things look off. We often use the anomaly prediction, allowing us to catch when a certain metric looks off. These changes are sometimes nonissues, but its nice to know about them anyway. We also use MC to get us lists of bot traffic from our site, which we can then exclude from our data.
QA Engineer`s experience with Monte Carlo
What do you like best about the product?
It's easy to manage dq checks;
the ability to integrate with github;
automatic search for anomalies (ex: a Large addition/ deletion of rows);
the ability to see a detailed description of the table;
the ability to view upstreams and downstreams;
technical support is very good and responds quickly.
the ability to integrate with github;
automatic search for anomalies (ex: a Large addition/ deletion of rows);
the ability to see a detailed description of the table;
the ability to view upstreams and downstreams;
technical support is very good and responds quickly.
What do you dislike about the product?
Too much visual content on pages;
table & field lineage slow down summary page;
features critical for our team are developing slow;
there are problems with integration with redshift.
table & field lineage slow down summary page;
features critical for our team are developing slow;
there are problems with integration with redshift.
What problems is the product solving and how is that benefiting you?
Data monitoring was not configured, due to which customers have being found the problems before us.
Now with MC we find a problems in a timely manner and quickly fix them
Now with MC we find a problems in a timely manner and quickly fix them
Product review for MonteCarlo
What do you like best about the product?
- Support relationship: Very reactive & super clear
- Product roadmap: Continous improvements
- Product roadmap: Continous improvements
What do you dislike about the product?
For now there are nothing particular to dislike
What problems is the product solving and how is that benefiting you?
- Data quality, lineage
- Interaction with data incidents
- More pro-active in finding issues
- People are more productive as they are able to identify the problem quickly
- Interaction with data incidents
- More pro-active in finding issues
- People are more productive as they are able to identify the problem quickly
Monte Carlo is THE leading tool for data observability!
What do you like best about the product?
Putting the power to be proactive, in the hands of the people who need it the most! Monte Carlo empowers the entire data team to take an aggressive stance in catching issues, instead of waiting for data incidents to happen to them.
Suport team is also proactive in addressing our issues as and when they arise.
Suport team is also proactive in addressing our issues as and when they arise.
What do you dislike about the product?
Minor UI glitches from time to time, and finnicky configuration options. The team is also not sitting in APAC region, which can make meetings and support hours with them a bit tricky to time.
What problems is the product solving and how is that benefiting you?
When errors arise in the infra layer or ELT layer, they sometimes are not caught there. Fortunately, Monte Carlo helps us monitor what's most important - the end product of the data pipelines, data itself! This gives us reassurance that at least there is a final layer of defense in catching data issues.
Useful tool in a data engineer's arsenal
What do you like best about the product?
1. Works out of the box (you don't need to tell it what exactly to monitor)
2. Customizable if you want more quality alerts
3. Inbuilt incident management features
4. Useful information about table usage across BI/DBT (we have used it many times for impact analysis)
2. Customizable if you want more quality alerts
3. Inbuilt incident management features
4. Useful information about table usage across BI/DBT (we have used it many times for impact analysis)
What do you dislike about the product?
1. No source / destination comparison (Not really dislike but I really wish this was possible in MC)
2. Ability to connect through ssh tunnel (again not really a dislike but I really wish it was possible)
2. Ability to connect through ssh tunnel (again not really a dislike but I really wish it was possible)
What problems is the product solving and how is that benefiting you?
1. Production data trend monitoring
2. Logs / infrequently monitored processes failing silently
3. Proactive identification of staleness / similar issues
4. Impact analysis
2. Logs / infrequently monitored processes failing silently
3. Proactive identification of staleness / similar issues
4. Impact analysis
Plug and play data observability with minimal configuration requred.
What do you like best about the product?
Plug and play - we were able to start getting value almost immediently on our platform, without needing a deep understanding of the data, or retrofitting lots of custom rules.
A fully-featured API means that we've been able to definine our monitors as code alongisde our dbt models.
The support team are responsive via Slack for any issues or queries that we do have.
A fully-featured API means that we've been able to definine our monitors as code alongisde our dbt models.
The support team are responsive via Slack for any issues or queries that we do have.
What do you dislike about the product?
I'd love to have broader coverage across our wider data platform, including kafka.
I'd also love a way to surface data quality scores into our BI tool alongside the data that's being reported.
I'd also love a way to surface data quality scores into our BI tool alongside the data that's being reported.
What problems is the product solving and how is that benefiting you?
Data observability, especially on an existing data platform.
Monte Carlo has been a great observability tool
What do you like best about the product?
I like the customization of monitors and the ability to share that across various teams. Monte Carlo easily integrates with Slack, dbt, and Snowflake. Making our job of catching data quality issues a lot easier.
What do you dislike about the product?
There has been a little confusion on my part about how to set up customized notifications. I think this is part of being newer to the tool and not using all of the resources.
What problems is the product solving and how is that benefiting you?
Monte Carlo is helping us identify data quality issues before they reach stakeholders. There are automated tests that run, which we don't have to configure a breach point, Monte Carlo learns what is normal over a given period and lets us know when something looks off.
Easy setting of alerts for monitoring data issues with SQL rulls
What do you like best about the product?
Easy to use, very efficient customer service and support, constantly improving and developing new features
What do you dislike about the product?
There are some features that would be helpful for us and still not available
What problems is the product solving and how is that benefiting you?
We use MC SQL rulls alerts to identify data issues. The alerting system is easy to use, and saves us time.
Monte Carlo increased accountability for our Engineering team
What do you like best about the product?
Visibility! Our Engineering team set up a system of alerts on the tables we care about that feed into multiple Slack channels that our Operations team members can watch and even comment on the incidents. Monte Carlo has improved the accountability within the Engineering team while providing higher quality of service to our business partners.
What do you dislike about the product?
-The table lineage images are very helpful. Any improvements to always see the full names would be appreciated.
-I would love to see more details surfaced for Tableau workbooks within Monte Carlo
-It would be great to immediately see a list of incidents open for you/your team upon log-in. What ends up happening for our teams is that incidents that need investigation or further research don't end up getting closed in Monte Carlo because its too tedious to go find them several hours or days later.
-The permissions roles are a little confusing. It seems (to me) they could be simplified into 3 groups: admin, editors, and viewers.
-I would love to see more details surfaced for Tableau workbooks within Monte Carlo
-It would be great to immediately see a list of incidents open for you/your team upon log-in. What ends up happening for our teams is that incidents that need investigation or further research don't end up getting closed in Monte Carlo because its too tedious to go find them several hours or days later.
-The permissions roles are a little confusing. It seems (to me) they could be simplified into 3 groups: admin, editors, and viewers.
What problems is the product solving and how is that benefiting you?
-It surfaces BigQuery log reporting, which is a skill we do not currently have in our engineering team
-Near real-time feedback on potential data issues for on-call Engineers to immediately dig into and resolve to keep lights-on and smooth reporting experience for the rest of the company & our clients
-We can easily see the lineage of content in our database (BigQuery) and connect it to reporting (in Tableau), which helps us identify and focus our attention on key assets and aids us when deprecating content
-Near real-time feedback on potential data issues for on-call Engineers to immediately dig into and resolve to keep lights-on and smooth reporting experience for the rest of the company & our clients
-We can easily see the lineage of content in our database (BigQuery) and connect it to reporting (in Tableau), which helps us identify and focus our attention on key assets and aids us when deprecating content
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