ALL COVERED TOPICS

NoSQL Benchmarks NoSQL use cases NoSQL Videos NoSQL Hybrid Solutions NoSQL Presentations Big Data Hadoop MapReduce Pig Hive Flume Oozie Sqoop HDFS ZooKeeper Cascading Cascalog BigTable Cassandra HBase Hypertable Couchbase CouchDB MongoDB OrientDB RavenDB Jackrabbit Terrastore Amazon DynamoDB Redis Riak Project Voldemort Tokyo Cabinet Kyoto Cabinet memcached Amazon SimpleDB Datomic MemcacheDB M/DB GT.M Amazon Dynamo Dynomite Mnesia Yahoo! PNUTS/Sherpa Neo4j InfoGrid Sones GraphDB InfiniteGraph AllegroGraph MarkLogic Clustrix CouchDB Case Studies MongoDB Case Studies NoSQL at Adobe NoSQL at Facebook NoSQL at Twitter

NAVIGATE MAIN CATEGORIES

Close

Hadoop: All content tagged as Hadoop in NoSQL databases and polyglot persistence

Hadoop distro for IBM's Mainframe

IBM and its partner Veristorm are working to merge the worlds of big data and Big Iron with zDoop, a new offering unveiled last week that offers Apache Hadoop running in the mainframe’s Linux environment.

3 hip hip hoorays for Hadoop on mainframes.

Original title and link: Hadoop distro for IBM’s Mainframe (NoSQL database©myNoSQL)

via: http://www.datanami.com/datanami/2014-04-17/inside_zdoop_a_new_hadoop_distro_for_ibm_s_mainframe.html


Hadoop and big data: Where Apache Slider slots in and why it matters

Arun Murthy for ZDNet about Apache Slider:

Slider is a framework that allows you to bridge existing always-on services and makes sure they work really well on top of YARN without having to modify the application itself. That’s really important.

Right now it’s HBase and Accumulo but it could be Cassandra, it could be MongoDB, it could be anything in the world. That’s the key part.

I couldn’t find the project on the Incubator page.

Original title and link: Hadoop and big data: Where Apache Slider slots in and why it matters (NoSQL database©myNoSQL)

via: http://www.zdnet.com/hadoop-and-big-data-where-apache-slider-slots-in-and-why-it-matters-7000028073/


Price Comparison for Big Data Appliance and Hadoop

The main differences between Oracle Big Data Appliance and a DIY approach are:

  1. A DIY system - at list price with basic installation but no optimization - is a staggering $220 cheaper as an initial purchase
  2. A DIY system - at list price with basic installation but no optimization - is almost $250,000 more expensive over 3 years.
  3. The support for the DIY system includes five (5) vendors. Your hardware support vendor, the OS vendor, your Hadoop vendor, your encryption vendor as well as your database vendor. Oracle Big Data Appliance is supported end-to- end by a single vendor: Oracle
  4. Time to value. While we trust that your IT staff will get the DIY system up and running, the Oracle system allows for a much faster “loading dock to loading data” time. Typically a few days instead of a few weeks (or even months)
  5. Oracle Big Data Appliance is tuned and configured to take advantage of the software stack, the CPUs and InfiniBand network it runs on
  6. Any issue we, you or any other BDA customer finds in the system is fixed for all customers. You do not have a unique configuration, with unique issues on top of the generic issues.

This is coming from Oracle. Now, without nitpicking prices — I’m pretty sure you’ll find better numbers for the different components — how do you sell Hadoop to the potential customer that took a look at this?

Original title and link: Price Comparison for Big Data Appliance and Hadoop (NoSQL database©myNoSQL)

via: https://blogs.oracle.com/datawarehousing/entry/updated_price_comparison_for_big


Hadoop analytics startup Karmasphere sells itself to FICO

Derrick Harris (GigaOm):

The Fair Isaac Corporation, better known as FICO, has acquired the intellectual property of Hadoop startup Karmasphere. Karmasphere launched in 2010, and was one of the first companies to push the idea of an easy, visual interface for analyzing Hadoop data, and even analyzing it using traditional SQL queries.

Original title and link: Hadoop analytics startup Karmasphere sells itself to FICO (NoSQL database©myNoSQL)

via: http://gigaom.com/2014/04/17/hadoop-analytics-startup-karmasphere-sells-itself-to-fico/


Hortonworks: the Red Hat of Hadoop

However, John Furrier, founder of SiliconANGLE, posits that Hortonworks, with their similar DNA being applied in the data world, is, in fact, the Red Hat of Hadoop. “The discipline required,” he says, “really is a long game.”

It looks like Hortonworks’s positioning has been successful in that they are now perceived as the true (and only) open sourcerers.

Original title and link: Hortonworks: the Red Hat of Hadoop (NoSQL database©myNoSQL)

via: http://siliconangle.com/blog/2014/04/16/hortonworks-the-red-hat-of-hadoop-rhsummit/


Apache Hadoop 2.4.0 released with operational improvements

Hadoop 2.4.0 continues that momentum, with additional enhancements to both HDFS & YARN:

  • Support for Access Control Lists in HDFS
  • Native support for Rolling Upgrades in HDFS
  • Smooth operational upgrades with protocol buffers for HDFS FSImage
  • Full HTTPS support for HDFS
  • Support for Automatic Failover of the YARN ResourceManager (a.k.a Phase 1 of YARN ResourceManager High Availability)
  • Enhanced support for new applications on YARN with Application History Server and Application Timeline Server
  • Support for strong SLAs in YARN CapacityScheduler via Preemption

Original title and link: Apache Hadoop 2.4.0 released with operational improvements (NoSQL database©myNoSQL)

via: http://hortonworks.com/blog/apache-hadoop-2-4-0-released/


Hydra takes on Hadoop

A good interview on InfoQ comparing Hadoop with AddThis’s open source Hydra:

What use case(s) is Hydra better suited for compared to Hadoop. When would Hadoop be a better choice?

Hydra is better at data exploration. You can follow a number of interesting leads from the results of a single, probably rather fast, map job. Queries on the resultant tree usually take on the order of seconds (or milliseconds).

Non-programmers can produce functioning products with a small amount of guidance. The web UI provides most everything that might be needed; it might be as simple as pressing clone on an existing job, changing the tree to use a couple different features and hitting go. In minutes they have a new URL endpoint to show your impressive new KPI on your company home page.

Hadoop has a few advantages though. It has stronger native support for very large, one-off joins. Technically speaking this just means more implicit sorting of files. Sorting huge numbers of things is expensive so we try pretty hard to avoid it, and as a result first order support for it is a little lacking. On the other hand, you might find that you don’t really need the full, perfect join and are instead content with a Bloom-filter-based probabilistic hybrid — in which case Hydra will once again save you some sweet cycles.

Original title and link: Hydra takes on Hadoop (NoSQL database©myNoSQL)

via: http://www.infoq.com/news/2014/04/hydra


Intel kills a Hadoop and feeds another

I seriously doubt you could have missed the 2nd part of this, but here’s the shortest executive summary:

  1. Intel has killed its own distribution of Hadoop — is there anyone that would disagree this is a good idea?
  2. Intel has invested $740mil in Cloudera (for 18%) — there’s no typo. 740 millions.

The main questions:

  1. where will Cloudera put the $900mil raised in the last round(s)?
  2. why Intel invested so much?

These questions were also asked by Dan Primack for CNN Money and after looking at different angles he comes out empty.

So let’s check other sources:

  1. TechCrunch has initially speculated that much of the investment went to existing shareholders.

    The post was later updated with a comment from Cloudera’s VP of marketing stating that the majority of the money went to the company. But no word on how they’ll be used.

  2. Reuters writes that Intel made the investment to ensure their leading position in server processors:

    Intel hopes that encouraging more companies to leap into Big Data analysis will lead to higher sales of its high- end Xeon server processors. The chipmaker believes that hitching its wagon to Cloudera’s version of Hadoop, instead of pushing its own version, will make that happen faster.

    Still no word on how Cloudera will be using the money.

  3. Derrick Harris for GigaOm writes that the deal makes a lot of sense for both companies1:

    Cloudera needs capital and Intel’s huge sales force to keep up its engineering efforts and grow the company internationally.

    As part of the deal, Cloudera will be an early adopter of Intel gear and will optimize its Hadoop software to run on Intel’s latest technologies. Intel will port some of its work into the Cloudera distribution and will maintain its own Hadoop engineering team that will work alongside Cloudera’s engineers to help unite the two company’s goals.

  4. Jeff Kelly for SiliconAngle emphasizes the same channel advantages:

    Cloudera’s biggest reseller partner is Oracle. Based on my reading of the Intel announcement, the deal is not an official reseller partnership, but Intel will “market and promote CDH and Cloudera Enterprise to its customers as its preferred Hadoop platform.” Not quite as nice as having the Intel salesforce closing deals for it, but Cloudera stands to gain significant new business from the arrangement.


So how about this short list on how this round will be used by Cloudera:

  1. a part goes for international expansion
  2. a larger part goes to early shareholders
  3. the largest part goes into acquisitions

As for Intel, what if this investment also sealed an exclusive deal for Hadoop-centric Cloudera-supported Intel-powered appliance?


  1. Insert snarky comment here about a $740m deal that would not make sense to one of the parties. How about not making sense to any of them? 

Original title and link: Intel kills a Hadoop and feeds another (NoSQL database©myNoSQL)


Scaling the Facebook data warehouse to 300 PB

Fascinating read, raising interesting observations on different levels:

  1. At Facebook, data warehouse means Hadoop and Hive.

    Our warehouse stores upwards of 300 PB of Hive data, with an incoming daily rate of about 600 TB.

  2. I don’t see how in-memory solutions, like Hana, will see their market expanding.

    In the Enterprise Data Warehouses and the first Hadoop squeeze, Rob Klopp predicted a squeeze of the EDW market under the pressure of in-memory DBMS and Hadoop. I still think that in-memory will become just a custom engine in the Hadoop toolkit and existing EDW products.

    On the always mentioned argument that “not everybody is Facebook”, I think that the part that is hidden under the rug is that today’s size of data is the smallest you’ll ever have.

    In the last year, the warehouse has seen a 3x growth in the amount of data stored. Given this growth trajectory, storage efficiency is and will continue to be a focus for our warehouse infrastructure.

  3. At Facebook’s scale, balancing availability and costs is again a challenge. But there’s no mention of network attached storage.

    There are many areas we are innovating in to improve storage efficiency for the warehouse – building cold storage data centers, adopting techniques like RAID in HDFS to reduce replication ratios (while maintaining high availability), and using compression for data reduction before it’s written to HDFS.

  4. For the nits and bolts of effectively optimizing compression, read the rest of the post which covers the optimization Facebook brought to the ORCFile format.

    There seem to be two competing formats at play: ORCFile (with support from Hortonworks and Facebook) and Parquet (with support from Twitter and Cloudera). Unfortunately I don’t have any good comparison of the two. And I couldn’t find one (why?).

Original title and link: Scaling the Facebook data warehouse to 300 PB (NoSQL database©myNoSQL)

via: https://code.facebook.com/posts/229861827208629/scaling-the-facebook-data-warehouse-to-300-pb/


Three opinions about the future of Hadoop and Data Warehouse

Building on the same data coming from Gartner and a talk from Hadoop Summit (exactly the same), Matt Asay1 and Timo Elliott2 place Hadoop on the data warehouse map.

Matt Asay writes in the ReadWrite article that Hadoop is not replacing existing data warehouses, but it’s taking all new projects:

Hadoop (and its kissing cousin, the NoSQL database) isn’t replacing legacy technology so much as it’s usurping its place in modern workloads. This means enterprises will end up supporting both legacy technology and Hadoop/NoSQL to manage both existing and new workloads […]

Of course, given “the effective price of core Hadoop distribution software and support services is nearly zero” at this point, as Jeff Kelly highlights, more and more workloads will gravitate to Hadoop. So while data warehouse vendors aren’t dead—they’re not even gasping for breath—they risk being left behind for modern data workloads if they don’t quickly embrace Hadoop and other 21st Century data infrastructure.

On his blog, Timo Elliott makes sure that there’s some SAP in that future picture and uses their Hadoop partner, Hortonworks to depict it:

No. Ignoring the many advantages of Hadoop would be dumb. But it would be just as dumb to ignore the other revolutionary technology breakthroughs in the DW space. In particular, new in- memory processing opportunities have created a brand-new category that Gartner calls “hybrid transactional/analytic platforms” (HTAP)

hadoopmodernarchitecture_thumb

The future I’d like to see is the one where:

  1. there is an integrated data platform. Note that in this ideal world, integrated does not mean any form of ETL
  2. it supports and runs in isolation different workloads from online transactions and bulk upload to various forms of analytics
  3. data is stored on dedicated mediums (spinning disks, flash, memory) depending on the workloads that touch it
  4. data would move between these storage mediums automatically, but the platform would allow fine tuning for maintaining the SLAs of the different components

  1. Matt Asay is VP of business development and corporate strategy at MongoDB 

  2. Timo Elliott is an Innovation Evangelist for SAP 

Original title and link: Three opinions about the future of Hadoop and Data Warehouse (NoSQL database©myNoSQL)


Thoughts on The Future of Hadoop in Enterprise Environments

In case you are looking for some sort of reassurance that big companies are into Hadoop, check SAP’s Innovation Evangelist, Timo Elliott’s perspective on the Hadoop market. It should be no surprise what he sees as the main trend:

Companies want to take advantage of the cost advantages of Hadoop systems, but they realize that Hadoop doesn’t yet do everything they need (for example, Gartner surveys show a steady decline in the proportion of CIOs that believe that NoSQL will replace existing data warehousing rather than augmenting it – now just 3%). And companies see the performance advantages of in-memory processing, but aren’t sure how it can make a difference to their business.

Original title and link: Thoughts on The Future of Hadoop in Enterprise Environments (NoSQL database©myNoSQL)

via: http://timoelliott.com/blog/2014/03/thoughts-on-the-future-of-hadoop-in-enterprise-environments.html


Continuent Replication to Hadoop – Now in Stereo!

Hopefully by now you have already seen that we are working on Hadoop replication. I’m happy to say that it is going really well. I’ve managed to push a few terabytes of data and different data sets through into Hadoop on Cloudera, HortonWorks, and Amazon’s Elastic MapReduce (EMR). For those who have been following my long association with the IBM InfoSphere BigInsights Hadoop product, and I’m pleased to say that it’s working there too.

Continuent is the company behing Tungsten connector and replicator products which, in their words:

Continuent Tungsten allows enterprises running business- critical MySQL applications to provide high-availability (HA) and globally reduntant disaster recover (DR) capabilities for cloud-based and private data center installations. Tungsten Replicator provides high performance open source data replication for MySQL and Oracle and is a key part of Continuent Tungsten.

Original title and link: Continuent Replication to Hadoop – Now in Stereo! (NoSQL database©myNoSQL)

via: http://mcslp.wordpress.com/2014/03/31/continuent-replication-to-hadoop-now-in-stereo/