Big Data solutions, such as Apache Hadoop and Apache Cassandra, are growing up and are in the process of moving out of a grassroots movement to widespread adoption. Unfortunately, the majority of the technical expertise still lies in the hands of the open source project contributors and most solutions are tackled from the bottom up, starting with the technical problems. The collateral that is presently available is largely from the social media giants that tout solutions built using 10,000 node clusters that process petabytes of data a day. The reality? The average person just cannot relate or intuitively draw parallels to their own business problems.
While Big Data solutions are worthwhile far before you reach petabyte scale data, just getting started can be a challenge in itself. New open source projects are being regularly released that tackle a variety of issues related to Big Data, some of which are just slightly different to existing technologies. Just how does one navigate the plethora of technologies to design workable solutions to business problems? What if you only have gigabytes or terabytes of "medium" data on a small cluster? This panel features Solution Architects from a variety of key companies in the Big Data space which will provide deep dive technical discussions on real solutions we've employed for our customers, across a variety of industries, starting with the business problems.
engineer @cloudera, #flume committer, distributed systems / data / hadoop. author of hadoop operations from o'reilly. bio from Twitter
Emerging Technologist. Inventor. Chair of Austin Big Data User Group. Apache Contributor. Adventurer. South African bio from Twitter
Founder of infochimps.com - I build tools to Organize, Explore and Comprehend massive data sources. Hooray for big data, scrabble and the red sox. bio from Twitter
Co-founder of DataStax - Products and Services for Apache Cassandra. bio from Twitter
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