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  • Hadoop Project Commercial Support Tracker July 2016

    There are now?15 projects supported by all 5?distributors I?track, and several have had new releases since April. Kafka is the newest addition, and I believe the remaining 4-supporter offerings, Mahout and Hue, will?remain unsupported by IBM, who has its own alternatives.

    –More–

    Hadoop Apache Project Commercial Support Tracker April 2016

    There are now 19 commonly supported projects: Avro, Flume and Solr join the group supported by all 5 distributors and other changes appear as well.

    For this version of the tracker (last updated in?December), I’ve made one sizable change: Pivotal has been dropped as a “leading distributor,” dropping the?number to five. Pivotal?relies on Hortonworks’ distro?(as does Microsoft) as its commercial offering now.

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    Strata Standards Stories: Different Stores For Different Chores

    Has HDFS joined MapReduce in the emerging “legacy Hadoop project” category, continuing the swap-out?of components that formerly answered the question “what is Hadoop?” Stores for data were certainly a focus at Strata/Hadoop World in NY, O’Reilly’s well-run, well-attended, and always impactful fall event. The limitations of HDFS, including?its append-only nature, have become inconvenient enough to push the community to “invent” something DBMS vendors like Oracle did decades ago: a bypass. After some pre-event leaks about its arrival, Cloudera chose its Strata keynote to announce Kudu, a new columnstore written in C++, bypassing HDFS entirely. Kudu?will use an Apache license and will be submitted to the Apache process at some undetermined future time.

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    Hadoop Projects Supported By Only One Distribution

    The Apache Software Foundation has succeeded admirably in becoming?a place where new software ideas are developed: today over 350 projects are underway. The challenges?for?the Hadoop user are twofold:?trying to decide which projects?might be useful in big data-related cases, and determining?which are supported by commercial distributors. In?Now, What is Hadoop? And What’s Supported??I list 10 supported by only one:?Atlas, Calcite,?Crunch,?Drill,?Falcon,?Kite,?LLAMA,?Lucene,?Phoenix?and?Presto. Let’s look at them a little more.

    –more–

    Now, What is Hadoop?

    This?perennial question resurfaced recently in a thoughtful blog post by?Andreas Neumann, Chief Architect of Cask, called?What is Hadoop, anyway?. Ultimately, after a careful deconstruction of the terms in the question, Andreas concludes with

    “Does it really matter to agree on the answer to that question? In the end, everybody who builds an application or solution on Hadoop must pick the technologies that are right for the use case.”

    We’ve agreed?from the beginning – that is the only answer?that really matters. Still, the question continues to come up?for ?end users of the stack and for vendors like Cask (it helps them think about what to support in their application development offering Cask Data App Platform (CDAP).

    Analysts too: I’ve discussed it?several times, including a post a year ago called?What Is Hadoop….Now? tracking the path?from 6 commonly supported projects in 2012 to 15 in June 2014, across a set of distributors that included Cloudera, Hortonworks, MapR and IBM. “Support” here means you pay for subscription that explicitly includes the named project.

    This year, the expansion process?has continued – and it?does?matter.

    –more on Gartner blog–

     

     

    Hadoop Questions from Recent Webinar Span Spectrum

    This is a joint post authored with Nick Heudecker
    There were many questions asked after the last quarterly Hadoop webinar, and Nick and I have picked a few that were asked?several times to respond to here.

    –More on my Gartner blog

    Which SQL on Hadoop? Poll Still Says “Whatever” But DBMS Providers Gain

    Since Nick Heudecker and I began our quarterly Hadoop webinars, we have asked our audiences what they expected to do about SQL several times, first in January?2014. With 164 respondents in that survey, 32% said “we’ll use what our existing BI tool provider gives us,” reflecting the fact that most adopters seem not to want to concern themselves overmuch with the details.

    –More on my Gartner blog

    Strata Spark Tsunami – Hadoop World, Part One

    New York’s Javits Center is a cavernous triumph of form over function. Giant empty spaces were everywhere at this year’s?empty-though-sold-out Strata/Hadoop World, but the strangely-numbered, hard to find, typically inadequately-sized rooms were packed. Some redesign will be needed next year, because the event was huge in impact and demand will only grow. A few of those big tent pavilions you see at Oracle Open World or Dreamforce would drop into the giant halls without a trace – I’d expect to see some next year to make some usable space available.

    So much happened, I’ll post a couple of pieces here. Last year’s news was all about promises: Hadoop 2.0 brought the promise of YARN enabling new kinds of processing, and there was promise in the multiple emerging SQL-on-HDFS plays. The Hadoop community was clearly ready to crown a new hype king for 2014.

    This year, all that noise had jumped the Spark.

    — This post is continued on my Gartner blog —

    Hadoop Is A Recursive Acronym

    Hopefully, that title got your attention. A recursive acronym – the term first appeared in the?book?G?del, Escher, Bach: An Eternal Golden Braid and is likely more familiar to tech folks who know Gnu – is self-referential (as in “Gnu’s not Unix.”) So how did I conclude Hadoop, whose name origin we know, fits the definition? Easy – like everyone else, I’m redefining Hadoop to suit my own purposes.?

    –more–

    What Is Hadoop….Now?

    In February 2012, Gartner published?How to Choose The Right Apache Hadoop Distribution?(available to clients). At the time, the leading distributors were Cloudera, EMC (now Pivotal),?Hortonworks (pre-GA),?IBM,?and?MapR. These players all supported six Apache projects: HDFS, MapReduce, Pig, Hive, HBase, and Zookeeper. Things have changed.

    –more–

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