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Review and inputs for Monte Carlo data observability software
What do you like best about the product?
Monte Carlo uses machine learning to identify data down time which I find is a remarkable feature.
What do you dislike about the product?
The costs involved in implementing such a feature-rich software can be one of the impediments in installing the software
What problems is the product solving and how is that benefiting you?
The software essentially helps as a tool to understand the costs incurred due to downtime, the assessment of data downtime in terms of related software installed and its affects on inventory management.
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Great tool for data visibility and security.
What do you like best about the product?
It helped us to understand potential issues before they actually get pretty serious, which eventually happened to be cost-effective. It also helped with data freshness.
What do you dislike about the product?
It had minor UI issues which spoilt the overall experience of using the entire tool. It also needs a revamp on alert customisation settings. Still lacks MS Teams integration.
What problems is the product solving and how is that benefiting you?
The overall experience was seamless with minimal configuration. It helped to identify issues and errors faster due to the constant monitoring of data freshness.
Monte Carlo adds value to the project
What do you like best about the product?
Useful in data insights in various data lakes
What do you dislike about the product?
Defining separate domains for two separate ENV's
What problems is the product solving and how is that benefiting you?
For the accurate monitoring of the various data lakes in one go, and to predict the various behaviours of the data.
Great product, great customer success.
What do you like best about the product?
Montecarlo has enabled us to get a grip on data reliability. Company metrics being late for the weekly executive review has basically stopped being a thing because of Montecarlo.
What do you dislike about the product?
It would be great if this tool covered more than just our data warehouse. We've diagnosed incidents based on the copy of production data in our DW. If MC covered our prod databases we'd have known sooner.
What problems is the product solving and how is that benefiting you?
Montecarlo surfaces anomalies in our data which often reveal problems with a pipeline.
Building trust in your data
What do you like best about the product?
Ease of use. It's a very frictionless setup allowing users to add new databases to monitor in minutes.
What do you dislike about the product?
Nothing to dislike in general but when observing data, latency can be an issue. There generally has to be a passage of time for an issue to become apparent.
What problems is the product solving and how is that benefiting you?
We run a Saas application across 16 databases in 11 Snowflake accounts. Monte Carlo helps us observe these in a single view
Smart way to stay up to date with data quality issues without writing a single test or query.
What do you like best about the product?
I love the automated AI detection of quality issues that just come to me without even having to think where and how to start monitoring my data or writing tests or queries to look for issues, I love how it all ends up in one place (ETL issues, dbt failures, database schema changes), the easily accessible field and table lineage that help me understand where some issues come from - or even help to model data and refactor old models.
What do you dislike about the product?
The only concern is the load on our database and, therefore, increased costs but after a small duscussion with the Mc team we were able to improve this too by tuning down the frequencies these checks run agains our db.
What problems is the product solving and how is that benefiting you?
Monte Carlo clearly shows impacted buisness reports in each of the detected incidents while also providing a degreee of importance of the table and its impact downstream, and allows us to give a heads up to the stakeholders early on when anything unexpected happens.
Great tool which covers our requirements
What do you like best about the product?
- high configuration abilities
- many predefined monitors
- usage of artificial intelligence
- monitors as code
- great support and collaboration with the MC team
- many predefined monitors
- usage of artificial intelligence
- monitors as code
- great support and collaboration with the MC team
What do you dislike about the product?
- no possibility to define groups for notifications purposes
What problems is the product solving and how is that benefiting you?
- less own monitoring implementation necessary
- self-learning observability moves the monitoring to the next level
- self-learning observability moves the monitoring to the next level
Ensuring Data Reliability with Monte Carlo
What do you like best about the product?
The most helpful aspect of Monte Carlo is its real-time data quality issue detection and resolution, which ensures accurate and reliable data. The platform is easy to use and provides advanced algorithms and machine learning capabilities for identifying and resolving data anomalies and outliers. Monte Carlo's customer support is responsive and helpful in addressing any issues that users encounter.
What do you dislike about the product?
Sometimes I have experienced occasional issues with data processing and the platform's user interface, although these issues seem to be infrequent and have been promptly resolved by the Monte Carlo support team.
What problems is the product solving and how is that benefiting you?
1. automatically detecting and resolving data quality issues in real-time
2. saving time and preventing costly errors that can arise from using inaccurate or incomplete data
3. improving overall data quality by identifying data anomalies and outliers
4. helping businesses become more data-driven and better equipped to make informed decisions based on high-quality data
2. saving time and preventing costly errors that can arise from using inaccurate or incomplete data
3. improving overall data quality by identifying data anomalies and outliers
4. helping businesses become more data-driven and better equipped to make informed decisions based on high-quality data
Key part of our data strategy
What do you like best about the product?
1. Easy to set up,
2. Integrates nicely with Slack.
3. Solves an issue around data observability that is not currently solved by existing tools.
4. Company passionate about data and visionary.
2. Integrates nicely with Slack.
3. Solves an issue around data observability that is not currently solved by existing tools.
4. Company passionate about data and visionary.
What do you dislike about the product?
Mostly limited to big data cloud data warehouses like Snowflake, Databricks. Product still maturing from reporting perspective.
What problems is the product solving and how is that benefiting you?
Visisbility into data frshness and detects issues with data we ingest such as changes in distributions. Allows us to focus on core mission and not creating testing suites on data observability
Monte Carlo - Clearcover
What do you like best about the product?
The anomaly alerts delivered to slack are highly beneficial, but I probably use metadata and lineage information from the catalog the most.
What do you dislike about the product?
The search results delivered to the screen in the catalog. It's not very intuitive, and sometimes the table or view you would expect to appear at the top of the list is buried further down the result list.
What problems is the product solving and how is that benefiting you?
Data Discovery and stakeholder utilization of key assets. Metadata captured by MC ensures my team produces the correct solutions for our stakeholders. By sharing definitions and common query info, direct questions to my team are decreased so that we can focus on solution delivery.
We also use anomaly detection slack alerts in our daily data quality checks. While these are reactive alerts, the engineering team should be aware before stakeholders make us aware.
We also use anomaly detection slack alerts in our daily data quality checks. While these are reactive alerts, the engineering team should be aware before stakeholders make us aware.
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