Data profiling, data quality reporting
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Data profiling, data quality reporting
Sometimes a project knows little about its data. IA is good at data profiling / data discovery. It can give insight into data about data type, format, uniqueness, completeness, frequency distribution, etc. The other powerful feature of IA is its ability to check data against business rules. It can give statistics on how many records violate a rule.
Data rules, column analysis, virtual tables
The interface is not the most friendly. Performance.
There are also these following features - documented in the user guide - but do not work:
1. Global Logical Variables (GLVs)
2. Migrating projects. Neither the internal method (Export/Import) nor the command line interface (CLI) method work 100%. They sometimes error out.
3. When you open a data rule and do no modifications, when you close it, IA asks if you want to save the changes, even if you did not make any. A bit disturbing when you know you did not change anything yet you start to doubt what you think you know.
My wish list for new features:
1. Ability to use functions on data sources. I do not understand how IBM could miss this. Data sources are not visible when coding custom expressions. For example if you have a field called CUSTOMER.ACCOUNT_NUM, you cannot code TRIM(ACCOUNT_NUM). My workaround is to create a variable in the rule definition then bind it in the data rule. Functions can only be applied to variables, not directly to fields. I have a rule where I do things to about 12 fields - concatenate, substring, length, coalesce, etc - and I had to make up 12 lines in the definition that do nothing but refer to these variables. I had to invent a rule so I coded seemingly useless rule conditions like address1 = address1 just so I have a variable for the field I want to code functions for. Huge oversight on the part of IBM.
2. Copy a data rule and modify the copy. Right now only rule definitions can be copied, not data rules. Sometimes I need to create two or more versions of the same rule. IA forces me to generate each of them from scratch. This is annoying when version 2 is only slightly different from version 1. If it took me an hour to code the original, it would take me close to that amount of time to code the new version. If I could copy and modify, the effort would only take maybe 5 minutes.
3. The date of last modification. IA only shows the date of creation which is generally useless. The last modification date is far more important and needs to be available and visible.
4. A file manager, a la Windows Explorer. I may want to see the list of rules and sort them by date of modification.
5. Enhanced dedup on output. Currently, IA can only exclude duplicates based on the entire record. It should allow deduping on a select set of columns.
6. Feature to select one record from multiple matches in a join. For instance, in Oracle SQL, one can FETCH FIRST ROW ONLY or use ROWNUM or TOP 1.
7. Ability to sort the output.
8. New virtual tables take a while to appear. You create one and the list doesn't list the new table. Wait 15 minutes or so and maybe it will be listed. Or log out and log back in.
Since 2008.
The tool sometimes crashes or freezes. But the latest version, 11.7, is more than stable than previous ones.
Customer Service:
Scale of 1 to 10: 8. While IBM is excellent at responding to inquiries, it is slow to implement much-needed software fixes. While that is common in the industry, I would still like to see IBM fix software bugs sooner.
Technical Support:
Same as customer service.
Positive
No never had the chance.
I have not been involved in setup but I understand it is very complex, not for the faint of heart.
Excellent!
I was not involved in the selection.
Get the latest version. Compare with competing products. Know that there are not many experts in the product and that they may pay a premium to hire them.
My main use case for Monte Carlo is smart data observability and lineage that saves hours of debugging. What I like most about Monte Carlo is its automated data observability and lineage capabilities.
Monte Carlo's lineage feature has helped me save hours of debugging because its machine learning-driven alerts are incredibly effective. It quickly learns our database behavior and catches anomalies, freshness issues, or volume drops before our downstream users even notice.
Monte Carlo helps us catch data errors and broken dashboards before our team or clients notice them. I also love the intuitive user interface, which makes it easy to trace an issue from a Looker dashboard all the way back to our Snowflake warehouse. It has saved our data engineers team countless hours of manual debugging.
The best features that Monte Carlo offers include a highly intuitive user interface that makes it easy to trace an issue from a Looker dashboard all the way to our Snowflake warehouse. The platform's machine learning-driven alerting is incredibly smart because it quickly learns our data's baseline behavior and catches anomalies, freshness issues, or volume drops before our downstream users even notice it.
Monte Carlo integrates seamlessly with the major cloud data warehouses. It can configure deeper integration with some legacy on-premise systems or niche BI tools, which is valuable.
Monte Carlo has positively impacted our organization as it is a critical tool that integrates seamlessly with major cloud data warehouses. It is easy to trace an issue, and it has saved our data engineer team countless hours of manual debugging. It also helps us catch data errors and broken dashboards before our team or clients notice them.
While the machine learning-driven alerting is powerful, I find that the initial tuning phase in a complex Databricks environment can result in some alert fatigue.
The alert fatigue I experience is due to needing manual tweaking upfront to ensure our Slack channels are not flooded with false positives for expected volume fluctuations or batch variations.
I would appreciate a more streamlined interface, along with clear navigation and better sign-posting between sections, as this would improve the overall user experience.
I have been working in my current field for five years.
Monte Carlo is stable, as I have not experienced much downtime or crashing.
Monte Carlo handles our growing data needs very well, making it quite scalable.
Monte Carlo's customer support is responsive and helpful. My experiences reaching out to them show that they were very quick to help and very professional.
I previously used Datadog as a different solution.
I switched from Datadog to Monte Carlo because Datadog was somewhat complex, especially with the multiple features. Additionally, the customer support team was not as responsive compared to Monte Carlo's support, and the price was higher when compared to Monte Carlo.
It was very easy to deploy Monte Carlo in our environment, with no challenges faced.
I have seen a return on investment because Monte Carlo has solved the challenge of monitoring ingestion health at scale. We are able to automatically track data freshness across hundreds of tables sourcing from multiple systems, which benefits us by eliminating manual data quality checks and providing real-time alerts the moment an ingestion pipeline lags, significantly reducing our data downtime.
Monte Carlo provides a reasonable price plan, so I find it highly affordable for any organization sizes.
My advice to others looking into using Monte Carlo is that it is a great tool for data quality and observability, ensuring our data is timely and complete. It is very user-friendly, combining low-code capabilities for business users with complex SQL for technical users. It is a highly recommendable tool. Monte Carlo is very easy to set up and very useful for detecting issues. Monte Carlo is a game changer. I would rate this product four out of five.
