This tutorial-the first of a two-part series-will provide an introduction to BDAS, the Berkeley Data Analytics Stack. BDAS is an open source, next-generation data analytics stack under development at the UC Berkeley AMPLab whose current components include Spark, Shark and Mesos. We will start by covering Spark, a high-speed cluster computing system compatible with Hadoop that can outperform it by up to 100x thanks to its ability to perform computations in memory. Spark provides concise, high-level APIs in both Scala and Java, and is in use at Foursquare, Conviva, Klout, Quantifind, and other companies. We will provide an overview of the Spark architecture, typical data analytics workflows (e.g., loading data from HDFS into memory and interactively querying it), and how users are applying Spark. In addition, we will also introduce Shark, a port of Apache Hive onto Spark that is compatible with existing Hive warehouses and queries. Shark can answer HiveQL queries up to 100x faster than Hive without modification to the data and queries, and is also open source as part of BDAS.
Ion Stoica is a Professor of Computer Science at UC Berkeley, where he does research on cloud computing and networked computer systems. Past work includes the Dynamic Packet State (DPS), Chord DHT, Internet Indirection Infrastructure (i3), declarative networks, replay-debugging, and multi-layer tracing in distributed systems. His current research includes resource management and scheduling for data centers, cluster computing frameworks, and network architectures. He is the recipient of a SIGCOMM Test of Time Award, the CoNEXT Rising Star Award, the PECASE Award, and the ACM doctoral dissertation award. Ion also co-founded Conviva, a startup to commercialize technologies for large scale video distribution.
Matei Zaharia is a fifth-year PhD student at UC Berkeley, working with Scott Shenker and Ion Stoica on topics in cloud computing, operating systems, networking, and algorithms for large-scale data processing. He is the lead developer of the Spark programming framework, and also a committer on Apache Mesos and Apache Hadoop. He got his undergraduate degree at the University of Waterloo in Canada.
Reynold Xin is a third-year PhD student in the AMP Lab at UC Berkeley. He leads the development of the Shark project, which won the Best Demo Award at SIGMOD 2012. He is also the recipient of the inaugural Best Demo Award at VLDB 2011 for his work on the CrowdDB system. Before graduate school, he worked on ads infrastructure at Google and distributed databases at IBM. His interests include data management systems, distributed systems, and algorithms for large-scale data processing.
Shivaram Venkataraman is a fifth year PhD student at the University of California, Berkeley and works with Mike Franklin and Ion Stoica at the AMP Lab. His research
interests are in design of storage systems and analytics platforms for big-data applications. Before coming to Berkeley, he completed his M.S at the University of Illinois, Urbana-Champaign.
Andy Konwinski is a postdoc in the AMPLab at UC Berkeley focused on large scale distributed computing and cluster scheduling. He co-created and is a committer on the Apache Mesos project that has been adopted by Twitter as their private cloud platform. He also worked with systems engineers and researchers at Google on Omega, their next generation cluster scheduling system. More recently, he lead the AMP Camp Big Data Bootcamp and has been contributing to the Spark project.
Tathagata Das is an Apache Spark committer and a member of the PMC. He is the lead developer behind Spark Streaming, which he started while a PhD student in the UC Berkeley AMPLab, and is currently employed at Databricks. Prior to Databricks, Tathagata worked at the AMPLab, conducting research about data-center frameworks and networks with professors Scott Shenker and Ion Stoica.
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