|By Business Wire||
|February 26, 2013 12:01 PM EST||
To ensure that more organizations and people can use the vast amounts of data being generated, collected and stored everyday – also known as “big data” – Intel Corporation announced the availability of Intel® Distribution for Apache Hadoop* software (Intel® Distribution). The offering, which includes Intel® Manager for Apache Hadoop* software, is built from the silicon up to deliver industry-leading performance and improved security features.
The ability to analyze and make sense of big data has profound potential to transform society by enabling new scientific discoveries, business models and consumer experiences. Yet, only a small fraction of the world is able to extract meaning from all of this information because the technologies, techniques and skills available today are either too rigid for the data types or too expensive to deploy.
Hadoop* is an open source framework for storing and processing large volumes of diverse data on a scalable cluster of servers that has emerged as the preferred platform for managing big data. With even more information coming from billions of sensors and intelligent systems also on the horizon, the framework must remain open and scalable as well as deliver on the demanding requirements of enterprise-grade performance, security and manageability.
“People and machines are producing valuable information that could enrich our lives in so many ways, from pinpoint accuracy in predicting severe weather to developing customized treatments for terminal diseases,” said Boyd Davis, vice president and general manager of Intel’s Datacenter Software Division. “Intel is committed to contributing its enhancements made to use all of the computing horsepower available to the open source community to provide the industry with a better foundation from which it can push the limits of innovation and realize the transformational opportunity of big data.”
Performance and Security: The Intel Difference
Intel is delivering an innovative open platform built on Apache Hadoop* that can keep pace with the rapid evolution of big data analytics. The Intel Distribution is the first to provide complete encryption with support of Intel® AES New Instructions (Intel® AES-NI) in the Intel® Xeon® processor. By incorporating silicon-based encryption support of the Hadoop Distributed File System*, organizations can now more securely analyze their data sets without compromising performance.
The optimizations made for the networking and IO technologies in the Intel Xeon processor platform also enable new levels of analytic performance. Analyzing one terabyte of data, which would previously take more than 4 hours to fully process, can now be done in 7 minutes1 thanks to the data-crunching combination of Intel’s hardware and the Intel Distribution. Considering Intel estimates that the world generates 1 petabyte (1,000 terabytes) of data every 11 seconds or the equivalent of 13 years of HD video, the power of Intel technology opens up the world to even greater possibilities.
For example, in a hospital setting, the intelligence derived from this data could help improve patient care by helping caregivers make quicker and more accurate diagnoses, determine effectiveness of drugs, drug interactions, dosage recommendations and potential side effects through the analysis of millions of electronic medical records, public health data and claims records. Strict guidelines also exist globally for protecting health and payment information, making it imperative to maintain security and privacy while performing analytics.
The addition of the Intel® Manager for Apache Hadoop* software also simplifies the deployment, configuration and monitoring of the cluster for system administrators as they look to deploy new applications. Using the Intel® Active Tuner for Apache Hadoop* software optimal performance is automatically configured to take the guesswork out of performance tuning. Until now, this required a specialized understanding of each application’s use of system resources along with the Hadoop configuration and performance benchmarks.
Intel is working with strategic partners to integrate this software into a number of next-generation platforms and solutions, and to enable deployment in public and private cloud environments. Partners supporting the launch include 1degreenorth*, AMAX*, Cisco*, Colfax Corporation*, Cray*, Datameer*, Dell*, En Pointe*, Flytxt*, Hadapt*, HStreaming*, Infosys*, LucidWorks*, MarkLogic*, NextBio*, Pentaho*, Persistent Systems*, RainStor*, Red Hat*, Revolution Analytics*, SAP*, SAS*, Savvis, a CenturyLink company*, Silicon Mechanics*, Simba Technologies*, SoftNet Solutions*, SuperMicro Computer, Inc.*, Tableau Software*, Teradata*, T-Systems*, Wipro* and Zettaset*.
A Comprehensive Approach to Big Data
The new software offering expands Intel’s extensive portfolio of datacenter computing, networking, storage and intelligent systems products. The recently introduced Intel® Intelligent Systems Framework, a set of interoperable solutions designed to enable connectivity, manageability and security across intelligent devices in a consistent and scalable manner, sets the foundation to help to gather, analyze and deliver valuable information for end-to-end analytics from the device to the datacenter.
Additionally, Intel continues to invest in research and capital to advance the big data ecosystem. Intel Labs is at the forefront of advanced analytics research which includes the development of Intel® Graph Builder for Apache Hadoop* software, a library to construct large data sets into graphs to help visualize relationships between data. Intel Graph Builder is optimized for the Intel Distribution to help reduce development time by eliminating the need to develop large amounts of custom code. Meanwhile, Intel Capital has been making major investments in disruptive big data analytics technologies including MongoDB company 10gen and big data analytics solution provider Guavus Analytics.
Intel (NASDAQ: INTC) is a world leader in computing innovation. The company designs and builds the essential technologies that serve as the foundation for the world’s computing devices. Additional information about Intel is available at newsroom.intel.com and blogs.intel.com.
Intel, the Intel logo and Intel Xeon are trademarks of Intel Corporation in the United States and other countries.
*Other names and brands may be claimed as the property of others.
1 Based on internal Intel tests
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products.
Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel.
Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
Notice revision #20110804
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The essence of data analysis involves setting up data pipelines that consist of several operations that are chained together – starting from data collection, data quality checks, data integration, data analysis and data visualization (including the setting up of interaction paths in that visualization). In our opinion, the challenges stem from the technology diversity at each stage of the data pipeline as well as the lack of process around the analysis.
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