Nathan Marz explains the ideas behind the Lambda Architecture and how it combines the strengths of both batch and realtime processing as well as immutability. Also: Storm, Clojure, and much more.
Hadoop, the distributive file system and MapReduce are just a few of the topics covered in this interview recorded live at QCon San Francisco 2013. Industry-standard Agile implementation and a lot of testing, assures the development team at Ancestry.com that they have an app that can handle the large traffic demands of the popular genealogy site.
Cliff Click explains 0xdata's H20, a clustering and in-memory math and statistics solution (available for Hadoop and standalone), writing H20's memory representation and compression in Java, low latency Java vs GCs, and much more.
Dean Wampler explains Scalding and the other Hadoop support libraries, the return of SQL, how (big) data is the killer application for functional programming, Java 8 vs Scala, and much more.
Eva Andreasson explains the various Hadoop technologies and how they interact, real-time queries with Impala, the Hadoop ecosystem including Hue, Oozie, YARN, and much more.
Barbara Liskov keynoted at QCon London 2013 on the power of abstraction. Afterwards, InfoQ caught up with up with her to ask her about language design, modularity and distributed computation.
Duncan Coutts on Parallelism and Concurrency with Haskell, Distributed Programming with Cloud Haskell
Duncan Coutts explains the nature of Concurrency and Parallelism in Haskell, its threading and STM implementation, Erlang OTP's influence on CloudHaskell for distributed programming, Monads, and 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.
Viktor Klang talks about the features of Akka 2.x and future releases, Akka's approach to fault tolerance, the effort to unify Futures in Scala, and the state of functional programming.
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.
Hive co-creator Ashish Thusoo describes the Big Data challenges Facebook faced and presents solutions in 2 areas: Reduction in the data footprint and CPU utilization. Generating 300 to 400 terabytes per day, they store RC files as blocks, but store as columns within a block to get better compression. He also talks about the current Big Data ecosystem and trends for companies going forward.
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.