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.

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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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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–

 

 

Perspectives on Hadoop Part Two: Pausing Plans

By Merv Adrian and Nick Heudecker 

In the first post in this series , I looked at the size of revenue streams for RDBMS software and maintenance/support and noted that they amount to $33B, pointing out that pure play Hadoop vendors had a high hill to climb. (I didn’t say so specifically, but in 2014, Gartner estimates that the three leading vendors generated less than $150M.)

In this post, Nick and I turn from Procurement to Plans and examine the buying intentions uncovered in Gartner surveys.

 

–more in 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

Hadoop Is A Recursive Acronym

Hopefully, that title got your attention. A recursive acronym – the term first appeared in the book Gödel, 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. 

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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.

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Hadoop is in the Mind of the Beholder

This post was jointly authored by Merv Adrian (@merv) and Nick Heudecker (@nheudecker) and appears on both of our Gartner blogs.

In the early days of Hadoop (versions up through 1.x), the project consisted of two primary components: HDFS and MapReduce. One thing to store the data in an append-only file model, distributed across an arbitrarily large number of inexpensive nodes with disk and processing power; another to process it, in batch, with a relatively small number of available function calls. And some other stuff called Commons to handle bits of the plumbing. But early adopters demanded more functionality, so the Hadoop footprint grew. The result was an identity crisis that grows progressively more challenging for decisionmakers with almost every new announcement.

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