Monte Carlo Data Observability Platform
Monte Carlo DataReviews from AWS customer
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Efficiency and Benefits of Using Monte Carlo for Data Quality Validation
What do you like best about the product?
Monte Carlo is a one-stop platform that allows us to access all data quality checks in a single location.
It saves time and significantly reduces manual effort during data validation.
Its is a testing automation tool, which runs predefined test cases and returns their result on a scheduled basis, it also helps tagging and maintaining failed test case and to report them
It saves time and significantly reduces manual effort during data validation.
Its is a testing automation tool, which runs predefined test cases and returns their result on a scheduled basis, it also helps tagging and maintaining failed test case and to report them
What do you dislike about the product?
Occasionally, due to the large volume of data, there can be some lag in script execution, resulting in delays when extracting the output. This can impact efficiency, although the tool remains highly effective overall
What problems is the product solving and how is that benefiting you?
- Its saving a lot of manual efforts while validating the data.
- Its very common for data observatibility, quality and monitoring the data.
- Its beneficial for filtering the anomalities based on different schema and at which particular layer our data has failed and anomaly is to fixed.
- Its easy to use and access and is very accomodating of changes and flexibility.
- Its very common for data observatibility, quality and monitoring the data.
- Its beneficial for filtering the anomalities based on different schema and at which particular layer our data has failed and anomaly is to fixed.
- Its easy to use and access and is very accomodating of changes and flexibility.
Monte Carlo has helped us close important gaps in our data quickly
What do you like best about the product?
What I like best about Monte Carlo is its ability to streamline the testing and automation process. Its scheduled test case execution and result reporting make managing tests more efficient and less time-consuming. I appreciate how it not only runs predefined test cases but also helps in organizing and tagging failed tests, making it easier to track and prioritize issues. This functionality ensures that teams can maintain a high level of test coverage, quickly identify problem areas, and improve the overall quality of the software. It's an excellent tool for maintaining a structured and automated testing workflow.
What do you dislike about the product?
It could be the way it is defined in our organization, but one thing I require is the ability to configure data & result on a source level and to have a percentage wise failure count and to configure an expected rate of failure for each source.
What problems is the product solving and how is that benefiting you?
Monte Carlo is solving several key problems in the testing and automation process. It eliminates the need for manual test execution by automating the running of predefined test cases on a scheduled basis, saving valuable time and reducing human error. The tool also helps in efficiently managing and tracking failed test cases by tagging them, which allows for better prioritization and faster issue resolution.
This benefits me by providing a more organized and streamlined approach to testing. I no longer need to manually run tests or constantly monitor test results. Instead, Monte Carlo handles the execution and reporting automatically, which frees up my time for more strategic tasks. The ability to quickly identify and address failed tests also leads to quicker feedback and continuous improvement of the software quality.
This benefits me by providing a more organized and streamlined approach to testing. I no longer need to manually run tests or constantly monitor test results. Instead, Monte Carlo handles the execution and reporting automatically, which frees up my time for more strategic tasks. The ability to quickly identify and address failed tests also leads to quicker feedback and continuous improvement of the software quality.
Good platform with difficult to discover / use features
What do you like best about the product?
clearly very powerful, monitors as code is very good; it is easy to get started with basic monitors and also more advanced monitors are fairly easy to set up once you have understood them
What do you dislike about the product?
features are often not easy to discover and use; some things are not transparent - for example, there often seem to be issues with which tables are shown and sometimes they can only be found in "All Domains" instead of the specific domain; sometimes API keys need to be re-generated for some reason because they seem to lose some permissions? I don't know if this is actually an issue of MonteCarlo or is more related to the integration by the platform team; it is hard to get business users to use it and collaborate with them - maybe MonteCarlo could do more outreach to them so they are more incentivized to use it
What problems is the product solving and how is that benefiting you?
monitor tables, define and measure SLIs/SLOs; make data quality visible to both data engineers and business stakeholders
Smooth, great UI/UX
What do you like best about the product?
The UI/UX, the design, themes, speed etc..
What do you dislike about the product?
Nothing, Nothing, Nothing, Nothing, Nothing.
What problems is the product solving and how is that benefiting you?
It is arranging all the data issues for us instead of doing in manually!
Excellent comprehensive data observability tool
What do you like best about the product?
Default monitors learn the patterns of our data and alert us to when something looks unusual. This has alerted us to many data quality and pipeline issues that we otherwise might have missed if we were to only set up manual monitors and tests. It is quite plug and play out of the box but also allows fine-grained control of custom monitors.
What do you dislike about the product?
The UI isn't the easiest to navigate and there is limited customisability.
Managing users, domains, audiences, and alerts has a learning curve and although this has been improved recently, it could still be more intuitive.
Managing users, domains, audiences, and alerts has a learning curve and although this has been improved recently, it could still be more intuitive.
What problems is the product solving and how is that benefiting you?
Monte Carlo is our primary data testing and observability tool, helping ensure we have high data quality, comprehensive testing across our whole data product, and timely alerting of issues.
Really good to use this tool
What do you like best about the product?
The way we can create dinamic reports on top o the tables, and also the ml that learns with the data to generate alerts.
What do you dislike about the product?
The notification message on the alerts was not working when I used it. It was a very powefull feature and it was not working.
What problems is the product solving and how is that benefiting you?
It solves the problem that we do not have to create several piplelines to monitor the data, with a couple clicks you can put several tables to be monitor. Also the dinamic monitoring.
Amazing platform to create alerting systems to quickly share visibility.
What do you like best about the product?
I like the possibility to create queries and use the results to create dynamic alerts via email, allowing us the possibility to customize anything we want to track. This is a deal breaker for one of our projects, allowing us to quickly share any issues with the respective stakeholders for each and all warnings.
What do you dislike about the product?
The lack of support/documentation related to the features was really a struggling point on the company, we were trying to use some features that were relative new and we face some issues, those issues were quite hard to get solved.
What problems is the product solving and how is that benefiting you?
allowing me to easily share visibility about data quality matters and issues happening on our pipelines.
mc review - leading a customer team
What do you like best about the product?
- generally, MC does a great job at making it really easy to get off the ground re: observability and alerting across the data landscape. you can get people and data onboarded quickly, and have basic alerting/thresholding/etc set up really fast, and that solves a large chunk of initial use cases.
What do you dislike about the product?
- focus feels too broad at times, between alerting/lineage/incident management/observability/etc it sometimes feels like a broad-strokes, jack-of-all-trades/master of none situation.
- often feels like if MC did something really, really well, it'd provide more value to us than doing 80% of a bunch of different jobs.
- often feels like if MC did something really, really well, it'd provide more value to us than doing 80% of a bunch of different jobs.
What problems is the product solving and how is that benefiting you?
MC allows us to have our end user teams onboard, observe, and manage their data well. Previously, there were a bunch of disparate in-house solutions that attempted to do this, and having it unified is great.
Data Quality Review
What do you like best about the product?
Monte Carlo has continued to add functionality to their monitoring capabilities. They are also rolling out an AI agent to help troubleshoot different data anomalies.
What do you dislike about the product?
The different type alerts can be tough to figure out which one fits a given situation.
What problems is the product solving and how is that benefiting you?
Monte Carlo is providing Data Quality information and alerts. It also helps to provide insight into upstream and downstream dependencies.
Robust Product that Increases Data Quality at Scale
What do you like best about the product?
Monte Carlo has allowed us to monitor our data pipelines with increased clarity. One of its standout features is its ability to catch errors before they reach production, significantly reducing downtime and ensuring data integrity.
This product also played a crucial role in supporting our new client-facing data product. Its robust error detection and comprehensive reporting capabilities enabled us to launch with confidence, knowing that our data was accurate and reliable.
This product also played a crucial role in supporting our new client-facing data product. Its robust error detection and comprehensive reporting capabilities enabled us to launch with confidence, knowing that our data was accurate and reliable.
What do you dislike about the product?
The learning curve for setting up monitors, and understanding the system, was steeper than expected. Combined with the large number of tables in our warehouse, it was a laborious implementation process. Some of these issues are unavoidable. In the future I'm curious if there's a more efficient way to set up monitors. For example, in our case we set up the exact same rules for multiple tables, with the only difference being the field name and some slight variations in the SQL.
What problems is the product solving and how is that benefiting you?
Catch errors before they hit our prod layer. Discover data quality issues that would've taken a substation effort outside of the platform.
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