Unravel Data is showcasing its full-stack, intelligent, and autonomous Application Performance Management (APM) for Big Data platform at O’Reilly Strata + Hadoop World, September 26-28 at the Javits Center in New York City. The platform optimizes, troubleshoots, and analyzes the performance of Big Data applications (ETL, Data Pipelines, Analytics, Business Intelligence, Machine Learning, etc.) running on the Big Data stack, and aims to empower Big Data operations and teams to deliver measurable business value.
Unravel Data, the industry’s only Application Performance Management (APM) platform designed for Big Data.
See Unravel in action. RSVP and stop by booth #237 for one of our scheduled group or one-on-one demos.
For schedule/availability of group and one-on-one demo’s between September 26-28 please visit here.
On Wednesday, September 27 from 5:25pm–6:05pm, join Unravel Data’s Adrian Popescu (Software Engineer) and Shivnath Babu (CTO) for a session titled, “Using ML to solve failure problems with ML and AI apps in Spark” at 1A 21/22.
The session will examine how to use the root cause diagnosis algorithm and methodology to solve failure problems with ML and AI apps in Spark. Adrian and Shivnath gathered the logs of a large set of applications and created algorithms to detect and resolve common problems automatically. With these tools and techniques, they were able to resolve application failure problems in seconds instead of weeks. For this session, they will share a categorization of application failures based on symptoms and root causes (e.g., resource limitations, incorrect coding practices, invalid inputs/outputs, Spark implementation issues, and others), as well as representative signatures for these failures, before demonstrating how to use the root cause diagnosis algorithm and methodology to alleviate the failures.
For additional details and to register for the session, please visit: http://conferences.oreilly.com/strata/hadoop-big-data-ny
O’Reilly Strata + Hadoop World New York 2017
655 W. 34th Street
New York, NY 10001