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The Monte Carlo platform speaks for itself - useful data anomaly detection from day 1
What do you like best about the product?
The most impactful benefit of using Monte Carlo is the detection of anomolies that would otherwise be near impossible to have visibility of. Onboarding is straightforward and you can realise benefits from day 1 with very little development effort. The product as a whole is well thought out and gives both the technical and non-technical user feature rich functionality. The low effort integrations to messaging platforms like Slack and workload management platforms like JIRA is a plus. In addition, it is good to see as a customer we have been listened to as our feature requests are being implemented on a frequent basis.
What do you dislike about the product?
This might be a symptom of any monitoring or observability platform, but the more monitoring you put in place, the more maintenance overhead is required to keep the noise to a minimum. However Monte Carlo are continuously developing features to mitigate this, for example, the misconfigured monitors daily digest feature to help the team keep on top of stale or incorrect monitors.
What problems is the product solving and how is that benefiting you?
Monte Carlo allows us to see direct data quality issues but also provides visibility on signals that might indicate more significant errors in our data pipelines. This allows us to reduce the number of issues reaching our data consumers and reduces the overal impact of errors in our reporting use cases. It also takes away a lot of the development effort required to set up alerts and notifications to alert our engineering teams of incidents. The incident management process is streamlined as a result of using Monte Carlo.
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Critical tool to have, with occasional quirks
What do you like best about the product?
Monte Carlo is best at providing critical data content testing and pattern recognition out of the box, wihch would be extremely time intensive to setup manually. Additionally, the ability to trace lineage on events, and identify affected dashboards and content is great. Customer support is also great, and are responsive to needs.
What do you dislike about the product?
Sometimes incidents can be erroneous, and nailing down the exact criteria to prevent "alert fatigue" can be tricky.
What problems is the product solving and how is that benefiting you?
The biggest piece is monitoring data content and patterns. You can quickly determine if an average for a fct metric is way out of whack from historical averages, if new values in enum are added, or schemas change. Many of these are out of the box, and take no effort. This is critical in a small team, where manpower is the limiting factor.
Best data observability platfotm
What do you like best about the product?
Useful and informative reports, simple interface, customizable monitoring of each source and database, real-time monitoring of data lineage
What do you dislike about the product?
For now everything is good, this tool is really helpful for me
What problems is the product solving and how is that benefiting you?
It helps us see data lineage to understand if there is an issue downstream or upstream.
We need less data analysts to actively monitor the reports
We need less data analysts to actively monitor the reports
Promising Data Observability Enterprise Tool
What do you like best about the product?
Monte Carlo made it easy for us to scale data quality at HubSpot, providing out-of-the-box monitors for freshness, volume, and schema changes, as well as the ability to set custom SQL monitors, either as code or within the platform. On top of this, Monte Carlo’s end-to-end lineage and other root cause analysis functionalities have given us the tools necessary to understand the impact and cause of data issues, which saves our team time and resources.
One of our biggest challenges was scaling the deployment of our dbt tests, and with Monte Carlo’s data observability platform, we can automatically roll out coverage across new data sources when we spin up new pipelines and dashboards. Their native dbt integration has also allowed us to monitor and detect when dbt models break so that we can quickly resolve the underlying issue.
One thing I appreciate about Monte Carlo is their committed customer success team and their willingness to listen and respond to product feedback. Data observability is a nascent area, and it’s helpful to have experts to assist as we grow and expand on our data quality strategy.
One of our biggest challenges was scaling the deployment of our dbt tests, and with Monte Carlo’s data observability platform, we can automatically roll out coverage across new data sources when we spin up new pipelines and dashboards. Their native dbt integration has also allowed us to monitor and detect when dbt models break so that we can quickly resolve the underlying issue.
One thing I appreciate about Monte Carlo is their committed customer success team and their willingness to listen and respond to product feedback. Data observability is a nascent area, and it’s helpful to have experts to assist as we grow and expand on our data quality strategy.
What do you dislike about the product?
The UI has undergone a significant (and welcome) facelift over the last several months. Early on, it could be difficult to toggle between views, but their redesign and homepage have made it easier to navigate and centralize our incident resolution workflows. There is still some work to go on sorting out the signals from the noise of alerts that can be triggered with MC but the company has been a great partner on working through these growing pains.
What problems is the product solving and how is that benefiting you?
Monitoring the health of our data assets in a centralized location.
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.
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