Amazon recently announced EMRFS, an implementation of HDFS that allows EMR clusters to use S3 with a stronger consistency model. When enabled, this new feature keeps track of operations performed on S3 and provides list consistency, delete consistency and read-after-write-consistency, for any cluster created with Amazon Machine Image (AMI) version 3.2.1 or greater.
Apache Flink has released the version 0.8.0 of their project. Besides the usual performance, compatibility, and stability improvements, it has also added a streaming Scala API, where streaming capabilities had so far been missing. Apache Flink has also been promoted to the top-level of the Apache projects recently after joining the incubator roughly nine months ago.
A number of Google researchers and engineers presented their view on the technical debt of using machine learning at a NIPS workshop. They identified different aspects of technical debt and came to the conclusion that without proper care, using machine learning or complex data analysis in your company can induce new kinds of technical debt different from classical software engineering.
Apache Spark 1.2.0 was released with Netty-based implementation, High Availability and Machine Learning APIs. It represents the work of 172 contributors from over 60 institutions and comprises more than 1000 patches. InfoQ talks with Patrick Wendell, a Spark committer and PMC member.
The latest version of big data analytics tools Splunk Enterprise and Hunk support instant pivot, enhanced event pattern detection, and prebuilt dashboard panels. Splunk Inc., provider of the software platform for operational intelligence, recently announced the general availability (GA) of version 6.2 of Splunk Enterprise and Hunk: Splunk Analytics for Hadoop and NoSQL Data Stores.
ThoughtWorks has published a digital preview of the January 2015 radar, providing opinion on techniques, tools, platforms and languages and taking a snapshot of the current trends in software technology.
Splice Machine version 1.0 supports analytic window functions and integration with Hadoop ecosystem. Splice Machine team recently released their Hadoop based RDBMS data management solution that can be used for transactional workloads on Hadoop.
Google announced earlier this year their Cloud Dataflow, a service and SDK for processing large amounts of data in batches or real time. Now they have open sourced the Dataflow Java SDK, enabling developers to see how it works and possibly use the SDK for services running on-premises or in other clouds.
LinkedIn recently open sourced Cubert, its High Performance Computation Engine for Complex Big Data Analytics. Cubert is a framework written for analysts and data scientists in mind.Developed completely in Java and expressed as a scripting language, Cubert is designed for complex joins and aggregations that frequently arise in the reporting world.
An agile view of Big Data, wherein data is viewed as a real time stream, offers a new look at how data is managed. Using an agile data infrastructure, organizations can conquer Big Data challenges with a level of ease, flexibility and performance. White paper by codeFutures describes the Agile view of Big Data.
At the 2014 QCon San Francisco conference, LinkedIn's Lin Qiao gave a talk on their Gobblin project (also summarized in a blog post) that is a unified data ingestion system for their internal and external data sources.
MapR Technologies, provider of the Apache Hadoop distribution, has open sourced their MapR-DB NoSQL database for unlimited production use. MapR-DB is a Wide Column NoSQL database with native integration to Hadoop and support for strong consistency and ACID transactions.
GridGain's In-Memory Data Fabric entered Apache Incubator last October under the name of Apache Ignite. The company donated its flagship in-memory computing platform to the Apache Software Foundation with the intention of attracting external developers and growing a viable community around its core technology.
At the StrataHadoop conference in Barcelona last week, Rod Smith, Vice President of the IBM Emerging Internet Technologies organization, presented work on an internal product they have been developing in their consulting work with clients that integrates data sources, and data analysis.
At the recent GOTO conference in Berlin, Mahout committer Sebastian Schelter outlined recent advances in Mahout's ongoing effort to create a scalable foundation for data analysis that is as easy to use as R or Python.