Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK
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

9:00–12:30 Tuesday, 22 May 2018
Modern real-time streaming architectures
Data engineering and architecture, Streaming systems and real-time applications
 Location: Capital Suite 8 Level: Intermediate
Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio), Ivan Kelly (Streamlio)
Average rating: ***..
(3.67, 3 ratings)
The need for instant data-driven insights has led the proliferation of messaging and streaming frameworks. Karthik Ramasamy, Arun Kejariwal, and Ivan Kelly walk you through state-of-the-art streaming frameworks, algorithms, and architectures, covering the typical challenges in modern real-time big data platforms and offering insights on how to address them. Read more.
16:35–17:15 Wednesday, 23 May 2018
Correlation analysis on live data streams
Data science and machine learning
 Location: Capital Suite 12 Level: Intermediate
Secondary topics:  Time Series and Graphs
Arun Kejariwal (Independent), Francois Orsini (MZ)
Average rating: ***..
(3.14, 7 ratings)
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.