TDWI Webinar Expert Panel Modern Data Pipelines
Expert Panel: Modern Data Pipelines TDWI Research VP Fern Halper hosts an expert panel with leading vendors to discuss modern data pipelines.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
A DataOps Observability Dialogue: Empowering DevOps for Data Teams
A DataOps Observability Dialogue: Empowering DevOps for Data Teams It used to be said that software is eating the world, but now data is running things. And it’s high-functioning data teams who make it all happen. But data teams are facing several obstacles that prevent them from delivering innovative analytics at today’s increased speed and scale. Software teams have been facing the same challenges for 10+ years and have tackled them with DevOps. So why are DataOps teams struggling when DevOps teams aren’t? They’re using the same tools to solve basically the same problem. . . .
Unravel Data Demo Video
Unravel’s Keith Alsheimer, Head of Marketing, and Chris Santiago, VP of Solutions Engineering, introduce Unravel’s DataOps observability platform: how it’s designed specifically from the ground up to empower data teams, why DevOps tools don’t work for DataOps, and a short demo of how Unravel AI helps accelerate troubleshooting, automatically where and exactly how to optimize performance, and proactively govern and reduce cloud costs with precision.
Takeaways from CDO TechVent on Data Observability
The Eckerson Group recently presented a CDO TechVent that explored data observability, “Data Observability: Managing Data Quality and Pipelines for the Cloud Era.” Hosted by Wayne Eckerson, president of Eckerson Group, Dr.
Tips to optimize Spark jobs to improve performance
Summary: Sometimes the insight you’re shown isn’t the one you were expecting. Unravel DataOps observability provides the right, and actionable, insights to unlock the full value and potential of your Spark application. One of the key features of Unravel is our automated insights. This is the feature where Unravel analyzes the finished Spark job and then presents its findings to the user. Sometimes those findings can be layered and not exactly what you expect.