Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY
Arun Kejariwal

Arun Kejariwal
Lead Engineer, Independent

@arun_kejariwal

Arun Kejariwal is an independent lead engineer. Previously, he was he was a statistical learning principal at Machine Zone (MZ), where he led a team of top-tier researchers and worked on research and development of novel techniques for install-and-click fraud detection and assessing the efficacy of TV campaigns and optimization of marketing campaigns, and his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection; and he developed and open-sourced techniques for anomaly detection and breakout detection at Twitter. His research includes the development of practical and statistically rigorous techniques and methodologies to deliver high performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been presented at international conferences and published in peer-reviewed journals.

Sessions

1:30pm5:00pm Tuesday, September 26, 2017
Secondary topics:  Architecture, Streaming
Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio), Arun Kejariwal (Independent), Neng Lu (Twitter), Sijie Guo (ASF)
Average rating: ***..
(3.00, 3 ratings)
Karthik Ramasamy, Sanjeev Kulkarni, Avrilia Floratau, Ashvin Agrawal, Arun Kejariwal, and Sijie Guo walk you through state-of-the-art streaming systems, algorithms, and deployment architectures, covering the typical challenges in modern real-time big data platforms and offering insights on how to address them. Read more.
4:35pm5:15pm Thursday, September 28, 2017
Secondary topics:  IoT, Streaming
Arun Kejariwal (Independent), Francois Orsini (MZ), Dhruv Choudhary (MZ)
Average rating: *****
(5.00, 3 ratings)
Services such as YouTube, Netflix, and Spotify popularized streaming in different industry segments, but these services do not center around live data—best exemplified by sensor data—which will be increasingly important in the future. Arun Kejariwal, Francois Orsini, and Dhruv Choudhary demonstrate how to leverage Satori to collect, discover, and react to live data feeds at ultralow latencies. Read more.