MapR is the best example I know of where a closed-source solution is beating open-source. For example, MapR's Hadoop distribution outperforms its open-source c

MapR is the best example I know of where a closed-source solution is beating open-source. For example, MapR's Hadoop distribution outperforms its open-source counterparts: * MapR breaks the Hadoop TeraSort world record using Google Compute Engine (https://plus.google.com/+GoogleDevelopers/posts/NURRXZ985XV https://plus.google.com/+GoogleDevelopers/posts/NURRXZ985XV) * Video: https://www.youtube.com/watch?v=9iQzMoy41_k https://www.youtube.com/watch?v=9iQzMoy41_k Also MapR-FS and its new M7 Tables are HDFS and HBase rewrites that remove the complexity of running HBase while improving performance: * M7 Overview: http://www.mapr.com/products/mapr-editions/m7-edition http://www.mapr.com/products/mapr-editions/m7-edition * Datasheet: http://www.mapr.com/Download-document/21-MapR-M7-Datasheet http://www.mapr.com/Download-document/21-MapR-M7-Datasheet * Benchmark: http://www.mapr.com/Download-document/52-MapR-M7-Performance.. http://www.mapr.com/Download-document/52-MapR-M7-Performance.... * Docs: http://doc.mapr.com/display/MapR/M7+-+Native+Storage+for+Map.. http://doc.mapr.com/display/MapR/M7+-+Native+Storage+for+Map.... * AWS Service: http://aws.amazon.com/elasticmapreduce/mapr/ http://aws.amazon.com/elasticmapreduce/mapr/ One reason behind MapR's competitive advantage is because it was co-founded by M.C. Srivas from the Google search team (http://www.wired.com/wiredenterprise/2011/12/ex-google-man/ http://www.wired.com/wiredenterprise/2011/12/ex-google-man/) so they have special knowledge on how the real GFS/MapReduce/BigTable stack is designed.