Founded as a pure-play Big Data company focused on Hadoop and Cloud, Karmasphere today boasts of a community of thousands of Hadoop data professionals using its products. Also, more than half of enterprise showing interest in Hadoop have turned to Karmasphere according to a few unverified research figures. Karmasphere Inc. recently announced Karmasphere 2.0, which focuses on self-service access to Big Data through a web-based social interface that facilitates team collaboration and reduces dependence on IT department. |
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Technical Specialities:
Hadoop, web analytics, business intelligence, programming languages, compilers, architecture, mathematics, database
Management Experience
Google, Yahoo, Ask, Ning, Omniture, BEA, Oracle, Sybase, Actuate, Apple, Zend, Intel, BMC
ProductsKarmasphere 2.0Karmasphere 1.8 Karmasphere for EMR Key Selling Points: - Increases Productivity for Data Analysts and Data Scientists working with structured and unstructured data in Hadoop - Reduces Hadoop learning curve for developers developing MapReduce jobs |
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PartnersAmazon »
Karmasphere and Amazon Web Services offer Big Data analytics in the
cloud to empower data professionals to jump start the analysis of
unstructured data in Hadoop using SQL skills without upfront fees or
long-term commitment. Amazon Elastic Map Reduce Amazon Elastic MapReduce
(EMR) is a web service that enables businesses,…
Cloudera »
By deploying Cloudera’s Distribution including Apache Hadoop (CDH)
in conjunction with Karmasphere Studio and Karmasphere Analyst,
enterprises are able to quickly meet their big data needs with a
familiar workflow in a powerful data environment. CDH is a
cost-effective, scalable, stable and fully supported big data platform
that allows enterprises…
Greenplum »
Karmasphere and Greenplum provide the Big Data Ecosystem a reliable
and stable Hadoop platform for Karmasphere Analytics. Greenplum provides
dependable performance in mission-critical environments, achieves two
to five times performance improvement compared to standard packaged
versions of Apache Hadoop and is easy to use with existing systems and
tools. Greenplum…
Hortonworks »
Karmasphere and Hortonworks work together to further the ubiquity of
Apache Hadoop. Through their collaboration Karmasphere and Hortonworks
streamline the process of deriving Big Data Intelligence from Hadoop.
Hortonworks Data Platform Hortonworks Data Platform, powered by Apache
Hadoop, is a massively scalable 100% open source platform for storing,
processing, and…
By working together to integrate IBM's implementation of Apache
Hadoop with Karmasphere products, IBM and Karmasphere deliver a seamless
out-of-the-box experience for data professionals. Together, Big Data
Analysis and development on the IBM Big Data Platform is completed
quickly and productively; increasing the ROI of enterprise Big Data
projects. IBM…
Karmasphere and MapR work closely to integrate Karmapshere’s
solutions with MapR’s Hadoop distribution, which emphasizes high
availability, fault tolerance, and enterprise-class support and service.
MapR MapR Technologies provides the industry’s most differentiated
distribution of software including Apache Hadoop to allow more
businesses to harness the power of Big Data analytics.…
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Quick Facts19200 Stevens Creek Blvd #130 Cupertino, CA 95014-2530, United States Phone: +1-650-292-6100 Employees (All Sites) 13 Year of Founding 2005 Annual Sales (Estimated) $1.30M Chariman: Martin Hall CEO: Gail Ennis Chief Financial Officer: Daniel Moskowitz CTO: Ben Mankin Vice President Engineering : Abe Taha Director: Manish Jiandani |
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As the
amount of data continues to grow exponentially, there has been increased focus
on stored data reduction methods. Data compression, single instance store and
data deduplication are among the common techniques employed for stored data
reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…
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