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Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY
Arun Kejariwal

Arun Kejariwal
Lead Engineer, Independent


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.


9:00am–12:30pm Tuesday, 09/11/2018
Location: 1E 12/13 Level: Intermediate
Secondary topics:  Data Platforms
Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio)
Average rating: ***..
(3.12, 8 ratings)
Arun Kejariwal and Karthik Ramasamy lead a journey through the landscape of state-of-the-art systems for each stage of an end-to-end data processing pipeline, covering messaging frameworks, streaming computing frameworks, storage frameworks for real-time data, and more. They also share case studies from the IoT, gaming, and healthcare and their experience operating these systems at internet scale. Read more.
1:15pm–1:55pm Wednesday, 09/12/2018
Location: 1A 12/14 Level: Intermediate
Secondary topics:  Media, Marketing, Advertising, Temporal data and time-series analytics
Arun Kejariwal (Independent), Francois Orsini (MZ)
Average rating: ****.
(4.00, 1 rating)
The rate of growth of data volume and velocity has been accelerating along with increases in the variety of data sources. This poses a significant challenge to extracting actionable insights in a timely fashion. Arun Kejariwal and Francois Orsini explain how marrying correlation analysis with anomaly detection can help and share techniques to guide effective decision making. Read more.