In recent years, organizations have begun to realize the competitive advantages offered by insights gained by strategically analyzing their Big Data archives. Extracting value from Big Data still remains a challenge for many organizations due to disparate data sources not communicating making it difficult to derive Business Intelligence from transferable data. Apache Hadoop™ was born out of a need to process an avalanche of big data. It is able to extract data from various sources, renovate it to fit into analytical requirements and load it into a data warehouse for consequent analysis. Apache Hadoop has developed as the de facto norm for processing Big Data.
This infrastructure offers a new approach to Big Data processing and analyzing which can scale down costs considerably. According to a recent report, “The revenue gained with Big Data solutions rose by 66% up to 73.5 billion euro worldwide over the past year.” Hadoop is one of the key technologies that creates a comprehensive data ecosystem comprised of distributed databases, query and workflow engines and much more. Apache Hadoop 2.0 has been launched which will offer significant improvements when using Hadoop for managing Big Data collections. A key advantage to Hadoop 2.0 is it is an open source project that is stable and easier to use. Many organizations are combining Hadoop with other solutions to improve their data analytics.
Hadoop 2.0 improves not only the way apps functions on Big Data platforms but also makes entirely new methods of data crunching possible that were previously available due to limitations in data architecture. This will principally establish the Hadoop 2.0 as the preferred platform with which the developers can prepare applications that will analyze data far more proficiently. It offers a comprehensive and integrated platform for Big Data storage and processing. The extensive nature of Hadoop 2.0 offers many enhanced query capabilities around data quality, integration, security, and governance issues that enterprises have been unable to resolve until now. Using Apache Hadoop, companies can process and export a huge amount of diverse data at scale. They can gain the additional capability to store and access data that they might require from information that was not previously loaded into the data warehouse. For example, the data scientists can utilize a large volume of source data from web logs, social media, and third party stores, stored on Hadoop to develop new analytical models that encourage new research and discovery. Further, they can store this data cost effectively in Hadoop and retrieve it as needed without affecting the EDW environment.
This state of the art Big Data technology is creating new opportunities and challenges for business across various industries. The challenge of data integration is one of the most vital issues that IT managers and CIOs face today – combining data from social media and other unstructured data into a traditional BI environment. Apache Hadoop provides a cost-effective and massively scalable platform for ingesting big data and preparing it for analysis. Using Hadoop to unload the traditional ETL practices can decrease the time of an analysis by hours or even days. Running the Hadoop cluster effectively means choosing an optimum infrastructure of networking, servers, storage, and software.
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