Systems | Development | Analytics | API | Testing

Unravel

3-Minute Recap: Unlocking the Value of Cloud Data and Analytics

DBTA recently hosted a roundtable webinar with four industry experts on “Unlocking the Value of Cloud Data and Analytics.” Moderated by Stephen Faig, Research Director, Unisphere Research and DBTA, the webinar featured presentations from Progress, Ahana, Reltio, and Unravel. You can see the full 1-hour webinar “Unlocking the Value of Cloud Data and Analytics” below. Here’s a quick recap of what each presentation covered.

Get Ready for the Next Generation of DataOps Observability

I was chatting with Sanjeev Mohan, Principal and Founder of SanjMo Consulting and former Research Vice President at Gartner, about how the emergence of DataOps is changing people’s idea of what “data observability” means. Not in any semantic sense or a definitional war of words, but in terms of what data teams need to stay on top of an increasingly complex modern data stack.

Webinar: Unlocking the Value of Cloud Data and Analytics

From data lakes and data warehouses to data mesh and data fabric architectures, the world of analytics continues to evolve to meet the demand for fast, easy, wide-ranging data insights. Right now, nearly 50% of DBTA subscribers are using public cloud services, and many are investing further in staff, skills, and solutions to address key technical challenges. Even today, the amount of time and resources most organizations spend analyzing data pales in comparison to the effort expended in identifying, cleansing, rationalizing, consolidating, and transforming that data.

The Data Challenge Nobody's Talking About: An Interview from CDAO UK

Chief Data & Analytics Officer UK (CDAO UK) is the United Kingdom’s premier event for senior data and analytics executives. The three-day event, with more than 200 attendees and 50+ industry-leading speakers, was packed with case studies, thought leadership, and practical advice around data culture, data quality and governance, building a data workforce, data strategy, metadata management, AI/MLOps, self-service strategies, and more.

Demo: Unravel Data - Automated Troubleshooting for Job Failures

For DataOps teams, job failures are common. But finding the issue is (traditionally) where things get even worse. It can take hours or days to troubleshoot a job failure. Unravel Data provides a single view where DataOps teams can locate exactly where–and why–a job failed, along with precise recommendations to troubleshoot the error. DataOps teams are now able to both diagnose and troubleshoot job failures in minutes instead of days or weeks.

Demo: Unravel Data - Data Pipeline Optimization (The Easy Way)

Data pipelines fail all the time for a variety of reasons; service downtime, data volume fluctuations, etc. Diagnosing these failures manually is very difficult and time consuming. Unravel Data allows DataOps teams to troubleshoot pipeline failures automatically – showing exactly where and why a pipeline failed, and precise recommendations to remedy the issues. Using Unravel, DataOps teams can now diagnose and fix data pipeline failures in a fraction of the time.

Demo: Unravel Data - Code-Level Insights for DataOps Teams

To ensure that jobs are running optimally, DataOps teams need to look at the detailed code. But DataOps teams don’t have the right tools to easily examine problematic code - or a simple path to optimizing it. With Unravel Data, DataOps teams can quickly troubleshoot applications that are throwing errors - all the way down to a specific line of problematic code. All in a single view.

Demo: Unravel Data - Allocating Costs with Precision Using the Enhanced Chargeback Report

DataOps teams need to understand where costs are going. But the reports provided by cloud vendors aren’t very granular - and they only get the reports after excess costs have been racked up. Unravel allows DataOps teams to understand where costs are going at a detailed level: by user, by service, by department. This information is captured and available as soon as a cluster is detected – allowing DataOps teams to take action and optimize in real time.