Spark has come about to represent the next big wave of Big Data Processing. First there was Hadoop with HDFS and Mapreduce. Now with many initatives to make Spark the next mapreduce, there seems to be a lot going on for this project that sprung from humble origins from the Amp Lab at UC Berkeley. With the founding of Databricks to support Spark, this open source top level Apache project has stepped into the limelight to garner it's share of fans and critics.
This talk will foster a lively discussion on Spark's inital goals, where it came from and what does the future look like for spark. Big Data vendor leaders are responding by introducing Spark’s capabilities into their architecture. Come join us for a lively panel discussion between the top Hadoop distribution vendors – Cloudera, MapR, and Pivotal – to hear their vision, strategy, and capabilities around Apache Spark. This will be a rare opportunity to see these four leading vendors on one panel, hear from their experts, and get their insight on best practices, real use cases, and solutions around Spark implementation.
Note: This event is free and will sell out so register ASAP.
Our panelists are
Sandy Ryza - Data Scientist at Cloudera - Sandy Ryza is a data scientist at Cloudera. He recently led Cloudera's Spark development and still contributes actively to the project. Prior to Spark, he worked on MapReduce and YARN, and is a member of the Hadoop Project Management Committee.
Sungwook Yoon, Data Scientist, MapR Technologies - Sungwook is a Data Scientist at MapR. Sungwook's data experience includes malware detection algorithms for packet stream analysis, mobile network signaling analysis, social network analysis, job title analysis as well as call center data analysis. Before joining MapR, Sungwook worked as an architect for Seven Networks, a company that delivers device-centric mobile traffic management and analytics for wireless carriers. Previously, Sungwook worked as a Research Scientist at Palo Alto Research Center, where he worked on projects for both DARPA and Xerox. Sungwook's main technical background lies in Artificial Intelligence and Machine Learning. His Artificial Intelligence reserach has been published in top-tier conferences and journals, including AAAI, ICAPS, NIPS, UAI, ICML, JAIR, and JMLR.
Gautam Muralidhar, Sr. Data Scientist, Pivotal - Gautam currently works as a Sr. Data Scientist at Pivotal, where he helps customers derive actionable insights from big data by solving machine learning challenges using state of the art analytics infrastructure and tools from Pivotal's stack. He focuses on the following industry verticals: healthcare, retail, and logistics. When not working with customers, he contributes to R&D activities at Pivotal, primarily focused on image analytics and computer vision, and on developing non-trivial machine learning algorithms such as stacked auto encoders (deep learning) and hierarchical Bayesian models on Pivotal's stack. His industry experience includes Philips Healthcare, where he developed software components for a large scale medical imaging platform for the storage, retrieval, and analysis of multi-modality medical imaging data. He has also worked at Oracle as an applications engineer, and at VuCOMP as an algorithm engineer in R&D, where he contributed to VuCOMP's FDA approved image processing and computer vision algorithms for computer-aided detection of breast cancer on mammography. Gautam holds a Ph.D degree from The University of Texas at Austin and his dissertation work spanned the areas of computer vision, machine learning, and medical imaging.
The moderator for the evening is Daniel Gutierrez
Daniel D. Gutierrez is a Los Angeles–based data scientist working for a broad range of clients through his consultancy AMULET Analytics. He’s been involved with data science and Big Data since long before it came in vogue, so imagine his delight when the Harvard Business Review deemed “data scientist” as the sexiest profession for the 21st century. He is also a recognized Big Data journalist serving as Managing Editor for insideBIGDATA.com, and is working on a new machine-learning book due out in later this year.
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