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![]() So, in a mid to large size hospital computer storage requirements, and associated computing power and network infrastructure performance will need to increase by at least three order of magnitude. ![]() Within ten years, a large genomic research program may need to analyze many petabytes to Exabyte of data.Īdding patient’s genomic date to patient Electronic Health Record (EHR) will increase per patient dataset size from at most a few Gigabytes (today) to several terabytes. Today, utilizing these technologies, a typical research program could generate from tens of terabytes to petabytes of data for a single study. By 2020 whole genome sequencing could cost about $200. Illumina CEO, recently announced availability of whole genome sequencing for just under $1000. This talk will highlight some of these features.įrom Terabytes to Exabytes, A paradigm Shift in Big Data Modeling, Analytics and Storage management for Healthcare and Life Sciences Organizations Ali Eghlima, Director of Bioinformatic, Expert BioSystems Abstract It packs a multitude of features that are important to scalability, inter-operability and adaptability in enterprises. Hadoop version 2 recently became available. Hadoop 2 : New and Noteworthy Sujee Maniyam, Big Data Consultant/Trainer, ElephantScale Abstract Here we contrast a couple of solutions and their trade-offs, including one that we deployed for a Hadoop service provider. Another interesting use case we cover is Hadoop as a service supplemented by valuable data from the Hadoop service provider. We discuss the subtle storage storage problems their solutions. How will Hadoop and Openstack work together? While use cases such as spinning up development or test clusters are obvious, one needs to avoid resource fragmentation. Second we focus on real-time and streaming use cases and the HDFS changes to enable them, such as moving from node to storage locality, caching layers, and structure aware data serving.įinally we examine the trend for on-demand and shared infrastructure, where HDFS changes are necessary to bring up and later freeze clusters in a cloud environment. We discuss the unique challenges of virtual machines and the need to move MapReduce temp storage into HDFS to avoid storage fragmentation. We start with HDFS architectural changes to take advantage of platform changes such as SSDs, and virtual machines. This talk describes HDFS evolution to deal with this flux. Further, cloud infrastructure, (public & private), and the use of virtual machines are influencing Hadoop. Hadoop’s usage pattern, along with the underlying hardware technology and platform, are rapidly evolving. 2016 Storage Developer Conference Speakersīig Data Trends and HDFS Evolution Sanjay Radia, Architect / Founder, Hortonworks Abstract.2016 Storage Developer Conference Presentations.2016 Storage Developer Conference Agenda.2016 Storage Developer Conference Abstracts.2015 Storage Developer Conference Speakers.2015 Storage Developer Conference Agenda.2015 Storage Developer Conference Abstracts.2014 Storage Developer Conference Agenda.2013 Storage Developer Conference Presentations.2013 Storage Developer Conference Agenda.2013 Storage Developer Conference Abstracts. ![]() ![]() 2012 Storage Developer Conference Agenda.2011 Storage Developer Conference Agenda.2010 Storage Developer Conference Presentations.2010 Storage Developer Conference Agenda. ![]()
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