Hadoop Conference Japan 2014
Tokyo, Japan
Tuesday, July 8, 2014

This is the fifth event organized by Japan Hadoop User Group.  This year's event is focused on parallel distributed processing framework, Apache Hadoop and it's ecosystem software.

 Join us and listen to technology leaders and experts share their valuable knowledge and insights into Hadoop and latest trends. 


Rethinking SQL for Big data – Don’t Compromise on Flexibility or Performance

M.C. Srivas View Bio

Can I reduce the time to value for my business users on Hadoop data?

How can I do SQL on semi-structured types?

How do I create and manage schemas for my data when the applications are changing fast?

What types of distributed systems problems do I have to solve when you move beyond traditional MPP scale to Hadoop scale?

Overall, a new way of thinking is needed to bring end-to-end agility with the BI/Analytics environments operating on Hadoop/NoSQL data. Along with the table stakes requirements to support broad eco system of SQL tools, close attention must be paid to the new requirements such as working with flexible and fast changing data models, semi-structured data and achieving low latencies on the scale of ‘big’ data. This session will cover how Apache Drill is driving this audacious goal to bring Instant, Self Service SQL natively on Hadoop/NoSQL data without compromising either the flexibility of Hadoop/NoSQL systems or the low latency required for BI/Analytics experience. It covers the exciting architectural challenges the Apache Drill community is working with, progress made so far and the roadmap.

Practical Machine Learning: Innovation in Recommendation using Mahout and Solr

Akihiko Kusanagi View Bio

Machine Learning is a critical tool used for gaining actionable insight, more accurate foresight, and relevant inferences into your ever-increasing amount of data. A widespread application of machine learning is the recommendation engine. Apache Mahout, a project to build scalable machine learning libraries, greatly simplifies the process of extracting recommendations and relationships from datasets.

In this session, Akihiko sheds light on a more approachable recommendation engine design and the business advantages for leveraging this innovative implementation style.


M.C. Srivas

Srivas is MapR's co-founder. Srivas ran one of the major search infrastructure teams at Google where GFS, BigTable and MapReduce were used extensively. He wanted to provide that powerful capability to everyone, and started MapR on his vision to build the next-generation platform for semi-structured big data. That vision is shared by all at MapR. Srivas brings to MapR his experiences at Google, Spinnaker Networks, Transarc in building game-changing products that advance the state of the art.

Akihiko Kusanagi

Akihiko joined MapR Technologies in 2013 after holding various positions at Sun Microsystems and EMC. Akihiko has a unique range of experiences in both infrastructure (server, network, storage, and OS), application development, database, and distributed systems. Akihiko brings his vast knowledge to customers through deployment and consultancy in big data solutions.