Skip to main content

Karmasphere - Ready for 2.0

By Desk Editor



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.

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 

Products

Karmasphere 2.0
Karmasphere 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

Partners

Amazon »

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,…
View>>
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…
View>>
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…
View>>
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…
View>>

IBM »

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…
View>>

MapR »

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.…          

Quick Facts


19200 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 
Labels:

Comments

Post a Comment

Popular posts from this blog

In-memory data model with Apache Gora

By Sachin Ghai
Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB. Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive.
Introducing Apache Gora Although there are var…
4 comments
Read more

Amazon DynamoDB datastore for Gora

By Sachin Ghai
What was initially suggested during causal conversation at ApacheCon2011 in November 2011 as a “neat idea”, would soon become prime ground for Gora's first taste of participation within Google's Summer of Code program. Initially, the project, titled Amazon DynamoDB datastore for Gora, merely aimed to extend the Gora framework to Amazon DynamoDB. However, it seem became obvious that the issue would include much more than that simple vision.

The Gora 0.3 Toolbox We briefly digress to discuss some other noticeable additions to Gora in 0.3, namely: Modification of the Query interface: The Query interface was amended from Query<K, T> to Query<K, T extends Persistent> to be more precise and explicit for developers. Consequently all implementors and users of the Query interface can only pass object's of Persistent type. Logging improvements for data store mappings: A key aspect of using Gora well is the establishment and accurate definitio…
1 comment
Read more

Data deduplication tactics with HDFS and MapReduce

By Desk Editor
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…
Post a Comment
Read more