Category Archives: Hadoop

Hadoop Ecosystem – A Quick Glance

What do Pig, Kangaroo, Eagle, and Phoenix have in common? Hadoop! We got some interesting technologies with curious names in Hadoop ecosystem. Azkaban is bloody wicked. H20 and Sparkling Water compete in the same space. Rethink, Couch, Dynamo, and Gemfire … Continue reading

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Decision Matrix for Big Data Tools and Technologies

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EHC Use Case – Hadoop as a Service

Hadoop can handle extremely large, unstructured data sets efficiently and at affordable cost, makes it a valuable technology for enterprises across a number of applications and fields. Market Analysis predicts that the market for Hadoop MapReduce is forecast to grow … Continue reading

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Cruising Data Lakes at Supersonic Speeds

Traditional workloads or second platform workloads for organizations go into File Shares on NAS, HPC on SAN, or Backup/Archive workloads to tape. They typically work with SMB, NFS, or FTP protocols. Emerging workloads like Hadoop referred as third platform pushes … Continue reading

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EMC Isilon and RainStor for Big Data Management

Big Data creates petabytes of data that organizations can readily mine to discover patterns and trends. Although Hadoop provides a comparatively inexpensive way to manage massive amounts of data, it is difficult to manage as the Hadoop cluster grows big. … Continue reading

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Enterprise Infrastructure for Hadoop

Hadoop sandboxes rely on commodity hardware with direct attached storage (DAS). These implementations make it difficult to scale out on storage separately as Hadoop requires three or more copies of data residing within the internal drive of a server unit. … Continue reading

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Hosting Big Data

Rackspace recently introduced its new Big Data hosting options – customize your configuration for managing big data platform, run Hadoop on the public cloud, or configure your own private cloud. Rackspace eliminates the complex process of building and maintaining a … Continue reading

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Difference between MapReduce 1.0 and MapReduce 2.0

Apache Hadoop, introduced in 2005 has a core MapReduce processing engine to support distributed processing of large-scale data workloads. Several years later, there are major changes to the core MapReduce so that Hadoop framework not just supports MapReduce but other … Continue reading

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Self-Service Data Access – Pivotal DD

Enterprise data resides in heterogeneous systems and of different data types. IT has its challenges to consolidate data in the right time. Also, many times it is difficult to know what data sources are required to access data. Pivotal DD … Continue reading

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Virtualizing Hadoop

HDFS, the “storage” and MapReduce, the “compute” are combined in traditional Hadoop model. If this Hadoop model is directly translated into a VM, it will affect the ability to scale up and down as the lifecycle of VM is tightly … Continue reading

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