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

10 Use Cases of Hadoop

Derrick Harris over GigaOM lists 10 domains where Hadoop is used: online travel, mobile data, e-commerce, energy discovery, energy savings, infrastructure management, image processing, fraud detection, IT security, health care. Even if almost 2 years old, Mike Pearce’s 10 Problems that can use Hadoop is still a very good list of the types of challenges that Hadoop can help with.

But speaking of ways in which Hadoop is used, I remembered one friend of mine that kept teasing me: are there any other examples of Hadoop usage beyond log processing1? If you know any other Hadoop usages don’t be shy and leave a comment or drop me an email.


  1. He was doing image processing at that time. 

Original title and link: 10 Use Cases of Hadoop (NoSQL database©myNoSQL)

via: http://gigaom.com/cloud/10-ways-companies-are-using-hadoop-to-do-more-than-serve-ads/


The Outer Limits of Data Warehouse Technology

The story of adopting Hadoop (through Zettaset) at Zions Bancorporation:

The quest for a solution began in 2009 with an investigation of Zion’s existing Microsoft and Oracle technologies, as well as other technologies within the firm and new solutions on the market, Wood relates. After developing a list of six potential vendors, he says, he and his team quickly focused on two Hadoop-based solutions. The team, Wood explains, recognized the potential in Hadoop for “making security decisions proactively rather than reactively, based on mining business intelligence and combining it with event data from security devices.”

Original title and link: The Outer Limits of Data Warehouse Technology (NoSQL database©myNoSQL)

via: http://www.banktech.com/business-intelligence/232600226?printer_friendly=this-page


Hadoop and Seismic Data Processing

Geophysicists have been pushing the limits of high-performance computing for more than three decades; they were early adopters of the first Cray supercomputers as well as the massively parallel Connection Machine. Today, the most challenging seismic data processing tasks are performed on custom compute clusters that take advantage of multiple GPUs per node, high-performance networking and storage systems for fast data access.

How many fields we’ve never heard of have handcrafted over years their own solutions to deal with big data that would fit so nicely in Hadoop today?

Original title and link: Hadoop and Seismic Data Processing (NoSQL database©myNoSQL)

via: http://www.cloudera.com/blog/2012/01/seismic-data-science-hadoop-use-case/


Hadoop Market Competition: comScore From Cloudera to MapR

Mike Brown (comScore CTO):

We could capitalize the purchase [of MapR] with an annual maintenance charge versus a yearly cost per node. NFS allowed our enterprise systems to easily access the data in the cluster.

Some interesting bits:

  • comScore runs a 1000+ self-hosted Hadoop cluster
  • comScore migrated from Cloudera to MapR in 2 days
    • the migration was accomplished by copying and reloading data
    • depending on the size of stored data, a better approach would a rolling migration—
  • comScore MapR’s Direct Access NFS feature, which exposes Hadoop Distributed File System (HDFS) data as NFS files which can then be easily mounted, modified or overwritten
  • comScore will continue to use Cloudera for training purposes
    • Question: what is the advantage of paying two providers and maintaining two different clusters?

As previewed by Cloudera-Hortonworks exchanges, the competition on the Hadoop market is becoming fierce. But at least this story involves companies that are actively involved in innovating and improving Hadoop. Not those that just want to monetize it.

Original title and link: Hadoop Market Competition: comScore From Cloudera to MapR (NoSQL database©myNoSQL)

via: http://searchdatamanagement.techtarget.com/news/2240112247/ComScore-moves-big-data-analytics-environment-from-Cloudera-to-MapR