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While I agree the quoted assertion is bogus, I disagree with your reasoning. There are many arbitrage opportunities that are being missed, whenever technology,

by jwr 13y ago

While I agree the quoted assertion is bogus, I disagree with your reasoning. There are many arbitrage opportunities that are being missed, whenever technology, science, or anything that requires thinking is involved. This is due to human stupidity and/or unwillingness to learn new things.

calibraxis 13y ago
And there's many cultural forces at play. Many managers would pay a premium for a large disposable supply of programmers, rather than a smaller more skilled supply. Bargaining power becomes a factor; the smaller labor supply may demand more decisionmaking power than you culturally want to allow.
leokun 13y ago
Can you name a specific arbitrage opportunity because I wouldn't mind making some money.
- espeed 13y ago
  Trade Hadoop for Spark (http://spark.incubator.apache.org/ http://spark.incubator.apache.org/). HBase for MapR M7 Tables: * 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-Benchmark http://www.mapr.com/Download-document/52-MapR-M7-Performance... * Docs: http://doc.mapr.com/display/MapR/M7+-+Native+Storage+for+MapR+Tables 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/
  - alanctgardner2 13y ago
    Just to point out, MapR isn't a slam dunk for a couple reasons. Their HBase implementation is very fast, but it involves using their whole Hadoop distribution. In my experience: - the management UI isn't as good as Cloudera Manager, or even Ambari in HDP2.0 - 3rd party support isn't there. If you want to use anything you find on GitHub, get ready to add support for MapRFS, and revert the API version back to 0.21 - there are bugs. Weird, specific bugs in the filesystem implementation which will bite you once every few weeks. This is one area where they lag the ASF project: many eyes do make these kind of things shallow. Their smaller install base and fewer developers means these things take a long time to notice and fix.
    - espeed 13y ago
      Their HBase implementation is very fast, but it involves using their whole Hadoop distribution...3rd party support isn't there. If you want to use anything you find on GitHub, get ready to add support for MapRFS, and revert the API version back to 0.21 The MapR team is sharp and responsive. It's founded by ex-Googlers from the search-infrastructure team (http://www.wired.com/wiredenterprise/2011/12/ex-google-man/ http://www.wired.com/wiredenterprise/2011/12/ex-google-man/). M7 Tables are designed to be a drop in for HBase, with better performance and without the HBase complexity (it's much easier to run M7 Tables as the primary datastore for online apps). M7 Tables support and certification for several key projects is coming along... Spark 0.7 already works with MapR M7 Tables with the Hive images that were released with the Eco-1310 (http://doc.mapr.com/display/components/Hive+Release+Notes http://doc.mapr.com/display/components/Hive+Release+Notes, http://answers.mapr.com/questions/7803/shark-spark-over-mfs http://answers.mapr.com/questions/7803/shark-spark-over-mfs). MapR is in the process of certifying Titan (https://github.com/thinkaurelius/titan/wiki https://github.com/thinkaurelius/titan/wiki), and it's going is going through final QA right now (https://groups.google.com/d/msg/aureliusgraphs/RTeFVssIvoI/mkUEla0MgCgJ https://groups.google.com/d/msg/aureliusgraphs/RTeFVssIvoI/m...).

by jwr 13y ago

calibraxis 13y ago

And there's many cultural forces at play. Many managers would pay a premium for a large disposable supply of programmers, rather than a smaller more skilled supply. Bargaining power becomes a factor; the smaller labor supply may demand more decisionmaking power than you culturally want to allow.

leokun 13y ago

Can you name a specific arbitrage opportunity because I wouldn't mind making some money.

espeed 13y ago
Trade Hadoop for Spark (http://spark.incubator.apache.org/ http://spark.incubator.apache.org/). HBase for MapR M7 Tables: * 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-Benchmark http://www.mapr.com/Download-document/52-MapR-M7-Performance... * Docs: http://doc.mapr.com/display/MapR/M7+-+Native+Storage+for+MapR+Tables 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/
- alanctgardner2 13y ago
  Just to point out, MapR isn't a slam dunk for a couple reasons. Their HBase implementation is very fast, but it involves using their whole Hadoop distribution. In my experience: - the management UI isn't as good as Cloudera Manager, or even Ambari in HDP2.0 - 3rd party support isn't there. If you want to use anything you find on GitHub, get ready to add support for MapRFS, and revert the API version back to 0.21 - there are bugs. Weird, specific bugs in the filesystem implementation which will bite you once every few weeks. This is one area where they lag the ASF project: many eyes do make these kind of things shallow. Their smaller install base and fewer developers means these things take a long time to notice and fix.
  - espeed 13y ago
    Their HBase implementation is very fast, but it involves using their whole Hadoop distribution...3rd party support isn't there. If you want to use anything you find on GitHub, get ready to add support for MapRFS, and revert the API version back to 0.21 The MapR team is sharp and responsive. It's founded by ex-Googlers from the search-infrastructure team (http://www.wired.com/wiredenterprise/2011/12/ex-google-man/ http://www.wired.com/wiredenterprise/2011/12/ex-google-man/). M7 Tables are designed to be a drop in for HBase, with better performance and without the HBase complexity (it's much easier to run M7 Tables as the primary datastore for online apps). M7 Tables support and certification for several key projects is coming along... Spark 0.7 already works with MapR M7 Tables with the Hive images that were released with the Eco-1310 (http://doc.mapr.com/display/components/Hive+Release+Notes http://doc.mapr.com/display/components/Hive+Release+Notes, http://answers.mapr.com/questions/7803/shark-spark-over-mfs http://answers.mapr.com/questions/7803/shark-spark-over-mfs). MapR is in the process of certifying Titan (https://github.com/thinkaurelius/titan/wiki https://github.com/thinkaurelius/titan/wiki), and it's going is going through final QA right now (https://groups.google.com/d/msg/aureliusgraphs/RTeFVssIvoI/mkUEla0MgCgJ https://groups.google.com/d/msg/aureliusgraphs/RTeFVssIvoI/m...).