Argsystems LLC. can help you use commercial cloud for deploying your Hadoop cluster. The extra on-demand capacity can be relinquished upon completing the queries. This approach is ideal for one-time use without the cost of purchasing numerous servers.
Hadoop/MapReduce provides deep analytics solutions for your organization. However the requirements for a significant number of servers to store data and then launch Map/Reduce jobs can be a tremendous cost center especially given the provisioning expenses for interconnect. A Cloud based approach on the other hand presents a ready pool of computing servers (although virtual severs) can prove very economical.
Hadoop/MapReduce brings High Performance Computing to the Web 2.0 data center to solve numerous business problems associated with analyzing voluminous quantities of data. This approach has proved most economical dealing with data-intensive applications. Large organization with servers already deployed in large numbers are prime candidate for using that technology. Organization without readily available servers have turned to commercial cloud provider to source the capacity required to operate their Hadoop clusters. In many cases pre-configured Hadoop/ Map/Reduce instances are available. They can be fired instantly, while cluster capacity can be incremented by commissioning more Hadoop instances.
Organization with a need for low cost scalable storage and a data analytics framework will be able to store large sets of data in Cloud offered storage volumes such as Amazon S3, while deploying Hadoop/MapReduce virtual machines instances. The scalability and rapid provisioning for cloud computing based Hadoop clusters brings tremendous infrastructure and operational cost savings.
The clear benefit in operational cost is that organization that only need jobs done periodically can buy utility-like capacity from cloud provider for that period of time.
Building on a fully Open Source stack
Hadoop/MapReduce helps large Web 2.0 data center store and analyze the increasingly voluminous amount of data produced. It offers a simplified model for processing very large amount of data in any number of meaningful ways. The HDFS offers concurrent access to the data distributed across any number of inexpensive computer systems . The Hadoop/MapReduce offers a programmable method for allowing systems to concurrently process the large set of data. This approach brings all the benefits of modern distributed high performance computing systems, aggregating clusters computing power to solve a wide variety of data-intensive problems. Hadoop/MapReduce is a very flexible and economical approach. First it abstracts the technical complexities associated with building and operating a dedicated high performance computing infrastructure; taking full advantage of inexpensive off-the-shelf components, scaling out seamlessly by adding nodes up to several thousands, integrating with web centric applications with Java or any other popular scripting language.
Hive offers a data warehouse solution on top of HDFS. The SQL-like capabilities that it provides are directly deployed on top of HDFS for querying and analyzing very large data sets. By loading and storing data from HDFS into SQL structured tables and partitions hives offers a familiar environment to business analysts and engineers to conduct analytics. Leveraging on the distributed nature of HDFS allows it to be a highly scalable data warehouse environment.
Nutch is a highly distributed crawler that can be employed by a Hadoop cluster to conduct customized searches. It is generally employed to find and graph web pages, parsing them by content in various format.
Hbase a fully open source and proven highly scalable distributed database
Hbase presents a parallel column-oriented database built as the storage engine for Hadoop Distributed Filesystem. Hbase provides utilities that facilitates combining MapReduce jobs to and fro. Scaling out is done by adding nodes to the cluster. For those reasons HBase functions as de-facto high performance storage engine for large dataset residing on HDFS. HBase is a highly scalable database that can deliver very high performance read-write operations on data-intensive applications.
