When it comes to building data and AI products you can trust, Monte Carlo, the leader of the data observability category, and Alation, the data intelligence leader, are better together.
“What’s wrong with this metric?”
“Why is my data late?”
“How did this pipeline break?”
“Can I trust the data feeding this model?
These fire drills and many more are regular occurrences for today’s data teams. In fact, our 2022 study with Wakefield Research suggests that data engineers spend two days or more per week firefighting data quality issues, and in the 2023 edition of our survey, we discovered that average time to resolve an incident increased by 166% YoY.
Clearly, there has to be a better way. The good news? With the right technologies and processes, data organizations can operationalize data quality at scale, reducing time and resources and increasing stakeholder confidence in their data and AI products.
Enter: data observability and data intelligence platforms, the solution to data trust. While data observability solutions like Monte Carlo provide the framework necessary to detect, resolve, and prevent data quality issues, data intelligence platforms like Alation surface these alerts directly to analysts in the catalog itself.
To this end, we’re excited to announce Monte Carlo and Alation’s integration and strategic partnership to help companies achieve data trust across the modern data stack. We’re also excited to announce Monte Carlo’s participation in the Alation Open Data Quality Initiative, a program designed to give Alation customers the freedom to choose the data quality or observability solution of their choice to expand data trust in the Alation platform.
Scaling data governance with reliability and trust
Trusted by more companies in the Fortune 500 than any other data observability solution, Monte Carlo gives data teams automated, end-to-end visibility into the health of their data and analytics at each stage of the data pipeline. Our industry-defining data observability platform ensures that your team is the first to know and solve when data quality incidents arise and provides the tools to conduct impact and root cause analysis for quick resolution.
With more than 500 enterprise customers, Alation empowers data teams to find, understand, and govern data by building a shared language around data context, policies, definitions, rules, and more, available in one centralized platform. With the new Monte Carlo and Alation integration, data consumers can quickly understand the health of a table or dashboard, building confidence and trust in critical assets before using them.
Mutual customers can leverage Monte Carlo and Alation together to drive data trust at scale by surfacing Monte Carlo’s data quality alerts directly in Alation. This integration makes it possible for teams to reduce time-to-detection and resolve data incidents by integrating data observability into their data governance workflows.
How it works
The Monte Carlo – Alation integration uses Alation’s APIs from its Open Data Quality Initiative to populate the Health tab on a table’s page within Alation. Both cloud and on-premise versions of Alation are supported.
Within Monte Carlo, navigate to Settings > Integrations. Scroll down to the Notifications and Collaboration section, click Create, and select Alation.
The integration sends anomalies (“events”) from Monte Carlo to Alation. These events are used to populate the Health section within Alation.
The columns in the Health section are predefined as part of Alation’s Open Data Quality Initiative. Monte Carlo has populated the columns as follows:
- Rule: the type of anomaly. This also contains a link to the corresponding monitor or Asset page in Monte Carlo.
- Object Name: a link to the summary page for the table in Alation. This column is predefined by Alation and not editable by Monte Carlo.
- Status: a green, yellow, or red indicator based on the Incident Status in the Value column. This will be red if the Value is “No Status,” yellow if “Investigating,” and green for all other values.
- Value: the Incident Status (e.g. Fixed, Expected, etc) of the most recent event of that type or monitor.
- Description: the “Description” of the custom monitor in Monte Carlo, or an indication if this is a type of automated monitor for Monte Carlo.
- Last Updated: the time that the entry was created or last updated, whichever is most recent.
Once the integration between Monte Carlo and Alation is set up, events will automatically begin to flow to Alation, creating entries in the Health section.
Entries are added or updated when any of the following occurs in Monte Carlo:
- An event is created and added to an incident. This will add or update the corresponding row.
- The status of an incident has changed. This will update the Status column in Alation of any of the events that were part of that Incident.
- The description of a custom monitor in Monte Carlo has changed. This will change the description of the monitor in Alation.
- A custom monitor is deleted. This will remove the row for the respective monitor in Alation.
- A table is removed from a custom monitor. This will remove the row from the respective table in Alation.
Learn more by visiting the Monte Carlo docs site.
As part of Alation’s Open Data Quality Initiative, we’re excited to continue pioneering the data observability category and ensuring data trust for teams everywhere. Learn more about the initiative here.
We’ll also be at Alation’s annual user conference, RevAlation Chicago on October 23 – 24, 2023. See you there!
If you’d like to learn more about Monte Carlo and Alation’s partnership and integration, reach out to Matt or request a demo.