Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Quick, reliable, and cost-effective ways to operationalize big data apps (sponsored by Unravel)

Shivnath Babu (Unravel Data Systems, Duke University), Madhusudan Tumma (TIAA)
1:10pm–1:50pm Thursday, 09/13/2018
Sponsored
Location: 1A 01/02
Average rating: ****.
(4.00, 1 rating)

What you'll learn

  • Explore common problems faced when attempting to operationalize big data apps in a quick, reliable, and cost-effective manner when and learn how to solve them

Description

Systems like Hadoop, Spark, Kafka, Impala, Redshift, and TensorFlow have made it easier than ever for enterprises to create or migrate apps to the big data stack, while the cloud has enabled them to spin up big data clusters on demand and autoscale them as needed. Thousands of apps are being generated every day in the form of ETL and modeling pipelines, business intelligence and data cubes, deep machine learning, graph analytics, and real-time data streaming. However, operationalizing these big data apps in a quick, reliable, and cost-effective manner remains a daunting task.

Developers and operational staff may not have the experience in distributed systems to tune apps for efficiency and performance. Diagnosing data pipeline failures or missed SLAs of IoT apps can be a laborious process that involves multiple stakeholders. Shivnath Babu and Madhusudan Tumma outline common problems and their causes and share best practices to find and fix these problems quickly and prevent such problems from happening in the first place.

This session is sponsored by Unravel.

Photo of Shivnath Babu

Shivnath Babu

Unravel Data Systems, Duke University

Shivnath Babu is the CTO at Unravel Data Systems and an adjunct professor of computer science at Duke University. His research focuses on ease of use and manageability of data-intensive systems, automated problem diagnosis, and cluster sizing for applications running on cloud platforms. Shivnath cofounded Unravel to solve the application management challenges that companies face when they adopt systems like Hadoop and Spark. Unravel originated from the Starfish platform built at Duke, which has been downloaded by over 100 companies. Shivnath has won a US National Science Foundation CAREER Award, three IBM Faculty Awards, and an HP Labs Innovation Research Award.

Photo of Madhusudan Tumma

Madhusudan Tumma

TIAA

Madhu Tumma is director of IT engineering at TIAA, where he focuses on database core services, engineering, infrastructure, operations, and strategy. Madhu has 25 years of experience working on various platforms and data technologies. Over his career, he has held senior IT positions at JPMorgan, Bear Stearns, AboveNet, and Merrill Lynch’s DAF Group (BOA). Madhu is the author/coauthor of three Oracle database management-related books as well as a speaker and subject-matter expert in cloud analytics, data privacy, server engineering, and database management.