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My experience of Monte Carlo product and its team.
What do you like best about the product?
The best part of Monte Carlo is its UI and UX, it's designed very well so that a new user can navigate and understand the tool quickly.
The tools AI engine which gets trained over the metadata of our Bigquery is also a proven product, its detection of anomalies and raising incidents on its own as well as the alert fatigue management is industry-leading and other Goverance products are playing catchup.
The tools AI engine which gets trained over the metadata of our Bigquery is also a proven product, its detection of anomalies and raising incidents on its own as well as the alert fatigue management is industry-leading and other Goverance products are playing catchup.
What do you dislike about the product?
So far after a year of using the product and onboarding all Business Departments and receiving their feedback, I do not have any reason to dislike the product.
What problems is the product solving and how is that benefiting you?
Below are the problems that the product is solving:
1. Immediate automatic detection of fault or anomaly in data.
2. Data Lineage up to the BI Layer, in our case Looker; with additional integration points with DBT and Airflow pipelines.
3. Customization capability for extending lineage for cases when the sources are coming from diverse systems such as message queues, HTTP Post, and no Governance tool in the market was able to integrate and detect automatically when I was studying other tools and their customization endpoints came with limitations and hurdles.
4. Data Pipeline error detection for DBT/Airflow.
5. Snooze for alerts, reduces alert fatigue.
6. Automatic incident creation and incident management.
7. Incident Observability.
8. Asset catalog and ability to build Data Dictionary.
9. Enables homogeneous KPIs and Data Definition.
10. Administration of the Governance Tools.
1. Immediate automatic detection of fault or anomaly in data.
2. Data Lineage up to the BI Layer, in our case Looker; with additional integration points with DBT and Airflow pipelines.
3. Customization capability for extending lineage for cases when the sources are coming from diverse systems such as message queues, HTTP Post, and no Governance tool in the market was able to integrate and detect automatically when I was studying other tools and their customization endpoints came with limitations and hurdles.
4. Data Pipeline error detection for DBT/Airflow.
5. Snooze for alerts, reduces alert fatigue.
6. Automatic incident creation and incident management.
7. Incident Observability.
8. Asset catalog and ability to build Data Dictionary.
9. Enables homogeneous KPIs and Data Definition.
10. Administration of the Governance Tools.
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Very helpful for our daily data monitoring
What do you like best about the product?
easy UI to use , integrating with other software such as Slack, easy
Good staff and costumer supports
Good staff and costumer supports
What do you dislike about the product?
The 500 breached rows returned maximum in the alert
What problems is the product solving and how is that benefiting you?
Monitoring daily data errors
Great software and team!
What do you like best about the product?
Monte Carlo has fundamentally changed how we triage data issues within our environment. It presents an easy-to-use software that gives you a single place to see the highlights on your data issue when an issue arrises. The support from the Monte Carlo team has been a real help too! I've regularly connected with thier internal teams (engineering, support, product) both to get assistance or to influence the roadmap. The MC team was able to deliver new features for me within 3--6 months of implementation. The documentation to get the integrations setup was very helpful and accurate. Monte Carlo is also delivering new features and wizards to make the setup process easier. We typically use the software a few days a week when it alerts us to issues or we are triaging something that came up from the insights team.
What do you dislike about the product?
Monte Carlo can be intimidating to setup and get configured with your data system. There are also many ways to setup monitors which requires time to review and adjust the results. We have had a few data issues appear that were not caught by the automated monitors, so I would recommend having custom monitors on critical tables and not just relying on the out-of-the-box, AI-based monitors.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us get a better understanding of what is happening to our data and alerts us when out of the ordinary trends appear.
Great tool for data reliability
What do you like best about the product?
Anomaly detection, key asset clarification, and lineage build.
We have a large number of ETL jobs but we don't have a good tool to govern those jobs until we have Monte Carlo. MC allows us to monitor thousands of our tables in the data warehouse.
We have a large number of ETL jobs but we don't have a good tool to govern those jobs until we have Monte Carlo. MC allows us to monitor thousands of our tables in the data warehouse.
What do you dislike about the product?
Recently we have some false alerts about the deleted tables, but support team helps us fix them quickly
What problems is the product solving and how is that benefiting you?
Anomaly detection, key asset clarification, and lineage build.
We have a large number of ETL jobs but we don't have a good tool to govern those jobs until we have Monte Carlo. MC allows us to monitor thousands of our tables in the data warehouse.
We have a large number of ETL jobs but we don't have a good tool to govern those jobs until we have Monte Carlo. MC allows us to monitor thousands of our tables in the data warehouse.
Really good for table Lineage analysis & historic operations
What do you like best about the product?
It's table lineage charts are really useful to spot depencies & check resources / linked assets across different platforms / software (i.e. ETLs, BI tools, etc)
What do you dislike about the product?
It gets stuck sometimes when checking queries done on some tables in the lineage view.
What problems is the product solving and how is that benefiting you?
Mainly identifying dependencies for database changes or backfills, as well as potential impacts from unexpected issues or new developments.
Also, it helps identifying who made changes or read from one table, which is helpful to identify stakeholders of new or core models.
Also, it helps identifying who made changes or read from one table, which is helpful to identify stakeholders of new or core models.
Great Data Observability tool
What do you like best about the product?
The dashboard tab is the one I like best
What do you dislike about the product?
Scanning of Assets and removing or adding them can sometime be tricky
What problems is the product solving and how is that benefiting you?
Data Governance and ensuring Data Quality
Scalable monitoring and proactive support
What do you like best about the product?
Monitoring hundreds of tables is made easier by the simple setup and machine learned rules that work out of the box. Coupled with the ability to drill down and create custom rules specific to our business allows the Data Engineering team to be aware of critical issues and reduce time to resolution.
Being able to link our pipeline, tables, and BI tool in one view also gives us visibility into the scale of each incident to determine business impact, and escalate to the relevant stakeholders.
Performance monitoring and integrations give us a holistic view of the data stack in our organisation.
Customer support have been responsive, and customer success works regularly with us to learn our challenges and make suggestions to better use the platform.
Being able to link our pipeline, tables, and BI tool in one view also gives us visibility into the scale of each incident to determine business impact, and escalate to the relevant stakeholders.
Performance monitoring and integrations give us a holistic view of the data stack in our organisation.
Customer support have been responsive, and customer success works regularly with us to learn our challenges and make suggestions to better use the platform.
What do you dislike about the product?
- The amount of data to work through can be challenging at first,
- UI has been a little dated in our initial engagement. This has improved
- Filtering which tables to enable monitoring, which impact cost, can be challenging.
- UI has been a little dated in our initial engagement. This has improved
- Filtering which tables to enable monitoring, which impact cost, can be challenging.
What problems is the product solving and how is that benefiting you?
- Monitoring for stale data
- Identifying data anomolies
- Discovering key issues
- Identifying data anomolies
- Discovering key issues
I've been working as a dwh developer at trendyol and we've been using the product for 1.5 year.
What do you like best about the product?
To be able to learn the incidents in our tables before our customers and to see the volume and freshness details in the UI. Easily receive notifications via Slack.The audience structure facilitates the notification process.customer support is very fast
What do you dislike about the product?
The domain section is manual and difficult to read. Not being able to see the history in case any data is deleted.We receive too many alarms from some notifications and cannot prevent them. Like normalized messages coming in the thread.
What problems is the product solving and how is that benefiting you?
When an incident occurred on our tables, we could not be informed before the customers. For example, the last modified date of a table may have passed or the flow may have stopped and new data may not have been written to the table. Here MC warns us early with the thresholds he has learned over time.
Monte Carlo has been a game-changer for the whole company in terms of data governance.
What do you like best about the product?
The table lineage feature makes the Data Engineer and Data Analyst team members workflow much more efficient and the whole data team feels more confident in their knowledge of the data assets.
The table metrics enables the realisation of quick actions: we can decide to clean-up unused tables without worrying about unknown consequences or we can focus on optimising the tables based on the importance score. As a Data Engineer, we simply could not have optimosed our costs and improved our platform without the help of Monte Carlo.
The customer support is always swift in their response and also our concerns are addressed without hesitation. We have received exceptional support especially from Neil Gleeson, but also from the development team.
It was very easy to integrate with our warehouse and we were able to customise the access level quickly and efficiently
The table metrics enables the realisation of quick actions: we can decide to clean-up unused tables without worrying about unknown consequences or we can focus on optimising the tables based on the importance score. As a Data Engineer, we simply could not have optimosed our costs and improved our platform without the help of Monte Carlo.
The customer support is always swift in their response and also our concerns are addressed without hesitation. We have received exceptional support especially from Neil Gleeson, but also from the development team.
It was very easy to integrate with our warehouse and we were able to customise the access level quickly and efficiently
What do you dislike about the product?
Due to the product being continously developed, we experienced some minor bugs or some features not working as intended in the beginning. These are mostly frontend related bugs, not so distruptive of the core workflows that we use Monte Carlo for.
The extensive useage of the product can lead to aleart-fatigue and false positive alrerts that can be distruptive of the day-to-day workflow. We have not been able to properly adjust our workflows to react to MC alerts, there is a high overlap of the MC alerts with our other alerting systems. The false positives - although not comparebale to true positives - were causing some confusion and decreased trust in MC alerts.
We could benefit from more customizability: In some cases we have a broken lineage caused by pipelines behaving in ways Monte Carlo cannot detect properly. If we had the option to customise the lineage we could fill these gaps without the need to adjust our pielines or Monte Carlo to build a complex extra feature.
The extensive useage of the product can lead to aleart-fatigue and false positive alrerts that can be distruptive of the day-to-day workflow. We have not been able to properly adjust our workflows to react to MC alerts, there is a high overlap of the MC alerts with our other alerting systems. The false positives - although not comparebale to true positives - were causing some confusion and decreased trust in MC alerts.
We could benefit from more customizability: In some cases we have a broken lineage caused by pipelines behaving in ways Monte Carlo cannot detect properly. If we had the option to customise the lineage we could fill these gaps without the need to adjust our pielines or Monte Carlo to build a complex extra feature.
What problems is the product solving and how is that benefiting you?
Monte Carlo enables data-driven business teams to increase their supervision over the data that they are working with. This means that they get more insight in how their data is process upstream: they can understand issues quicker and approch the data team with clearer instructions. Setting up alerts helps them react to problems faster and their confidece of the data quality also increases. Since Monte Carlo is user frendly and customisable, they can potentially react to business problems they had not have the capability to monitor before. Another aspect is that Monte Carlo creates an "interface" between business and data teams. Business teams get a better view onto the "data product" that data teams offer, which increases the cooperaation and understanding between the two domains.
For data teams Monte Carlo solves also solves other problems, namely the governing capability over the data assets. With hunderds of pipelines and data assets in the thousands, it is easy to lose sight of what assets are important and what is the relation between these assets. The generated lineage graph is a game-changer for data teams which reduces the time to debug and understand certain processes significantly. It also helps optimise the data platform: we can confidently clean up unused assets to save costs and reduce noise. Data teams can also resove issues that previously went undetected which saves many hours spent debugging from both business and tech side.
For data teams Monte Carlo solves also solves other problems, namely the governing capability over the data assets. With hunderds of pipelines and data assets in the thousands, it is easy to lose sight of what assets are important and what is the relation between these assets. The generated lineage graph is a game-changer for data teams which reduces the time to debug and understand certain processes significantly. It also helps optimise the data platform: we can confidently clean up unused assets to save costs and reduce noise. Data teams can also resove issues that previously went undetected which saves many hours spent debugging from both business and tech side.
Data Governance with MonteCarlo
What do you like best about the product?
-> table & field lineage
-> out-of-the-box monitors
-> highly customizable monitor options
-> out-of-the-box monitors
-> highly customizable monitor options
What do you dislike about the product?
-> limited to BQ & BI tools. Integration with GCS/PubSub/Dataflow (or equivalent services) would definetely be a plus
What problems is the product solving and how is that benefiting you?
MonteCarlo is helping with monitoring our airflow pipelines and sending alerts to a dedicated channel.
Volume/freshness monitors help not to miss important issues.
Lineage tracing is useful in following data transformations.
The insights reports (like clean-up suggestions) help with cleaning the datawarehouse periodically.
Volume/freshness monitors help not to miss important issues.
Lineage tracing is useful in following data transformations.
The insights reports (like clean-up suggestions) help with cleaning the datawarehouse periodically.
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