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Sameer Agarwal

Sameer Agarwal
PhD Student, UC Berkeley

Sameer Agarwal is a final year Ph.D. student in the AMPLab at Berkeley working on large-scale approximate query processing frameworks. His research interests are at the intersection of distributed systems, databases and machine learning, and he has published over 10 articles in various top-tier conferences including NSDI, EUROSYS, SIGMOD, VLDB and KDD. He received his B.Tech in Computer Science and Engineering from the Indian Institute of Technology and was awarded the President of India Gold Medal in 2009. He was supported by the Qualcomm Innovation Fellowship during 2012-13 and is supported by the Facebook Graduate Fellowship during 2013-14.


Hadoop & Beyond Rhinelander South
Tutorial Please note: to attend, your registration must include Tutorials on Monday.
Tathagata Das (Databricks), Haoyuan Li (Alluxio), Ion Stoica (UC Berkeley), Reynold Xin (Databricks), Sameer Agarwal (UC Berkeley)
Average rating: ****.
(4.80, 10 ratings)
An introduction to the open-source Berkeley Data Analytics Stack (BDAS). Spark is a high-speed cluster computing engine that supports rich analytics (e.g. machine learning) and lower-latency processing (e.g. streaming). Tachyon provides in-memory storage, letting Spark and Hadoop jobs share data efficiently. Shark and GraphX provide high-speed Hive SQL queries and graph processing on top of Spark. Read more.


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