Big Data means more than just the size of a dataset. Pavlo Baron explains different ways of applying Big Data concepts in various situations: from analytics, to delivering content, to medical applications. His larger vision for Big Data ranges from specialized Data Scientists, to learning Decision Support Systems, to helping mankind itself.
Erik Meijer explains the various aspects needed to categorise data stores, how reactive programming fits in with databases, the return to data transformation, denotational semantics, and much more.
Eli Collins discusses Cloudera's CDH4 release, which tasks are well suited for Hadoop, Hadoop and MapReduce vs SQL, the state of Hadoop, and much more.
Stuart Halloway explains Datomic, programming transactional behavior with Datomic, Datalog and logic programming, programming with values, Clojure Reducers and much more.
Max Sklar talks about machine learning at Foursquare, the use of Bayesian Statistics and other methods to build Foursquare's recommendation system and much more.
In this interview at QCon London, LinkedIn’s Sid Anand discusses the problems they face when serving high-traffic, high-volume data. Sid explains how they’re moving some use cases from Oracle to gain headroom, and lifts the hood on their open source search and data replication projects, including Kafka, Voldemort, Espresso and Databus.
Ron Bodkin of Big Data Analytics discusses early adoption of Hadoop, NoSQL and big data technologies. He discusses common patterns and explains how developers can write low-level primitives to optimize MapReduce function. Other topics include Hive, Pig, multi tenancy, and security.
In this interview Ted Dunning talk about Hadoop, its current usage and its future. He explains the reasons for Hadoop's success and make recommendations on how to start using it.
In this interview recorded at JavaOne 2011 Conference, Spring Hadoop project lead Costin Leau talks about the current state and upcoming features of Spring Data and Spring Hadoop projects. He also talks about the Caching and Data Grid architecture patterns.
Ville Tuulos talks about Disco, the Map/Reduce framework for Python and Erlang, real-world data mining with Python, the advantages of Erlang for distributed and fault tolerant software, and more.
Rob Pike discusses concurrency in programming languages: CSP, channels, the role of coroutines, Plan 9, MapReduce and Sawzall, processes vs threads in Unix, and more programming language history.
Ron Bodkin discusses big data architecture, real-time analytics, batch processing, map-reduce, and data science.