Most Firms Use Hadoop for Big Data Management
With Yahoo’s formation of Hortonworks and Cloudera’s latest initiatives, Apache Hadoop has been widely used by enterprises for big data management these days. The Ventana Research survey has found many interesting trends and statics, looking at the overall adoption of Hadoop by various organizations. Their latest survey indicates that about 54% of organizations are using or considering Hadoop for managing big data.
The research was sponsored by Karmasphere, a Big Data Intelligence company we’ve become quite familiar with, along with Cloudera and Pervasive Software as co-sponsors. Ventana Research, a benchmark research and advisory services firm, found that 94% of Hadoop users perform analytics on large volumes that wasn’t possible before. Eighty-eight percent of Hadoop users analyze data in greater detail, and 82% can retain more of their data. Moreover, 87% of Hadoop users are performing and planning new types of large scale data analyses, and 63% of organizations use Hadoop to optimize their unstructured data, such as event logs.
The majority of the Hadoop users were able to create new products with big data, and go on to earn cost savings. Eighty-two percent of Hadoop users feel that they have benefited from optimized bug data analyses and utilization of computing resources.
Respondents of the survey were IT managers, developers, and data analysts from hundreds of organizations of different sizes at various industries. The Ventana Research survey tries to identify trends, maturity, challenges, best practices and technologies used organizations in managing big data.
Apparently, there has been already wide adoption of Apache Hadoop amongst enterprises. However, Hadoop advocates may find emerging competition in Microsoft, which now enters the data analytics realm with its Hadoop alternative, Project Daytona. Developed by eXtreme Computing Group at the Microsoft Research, the Project Daytona is designed to support a wide class of data analytics. It can scale to hundreds of server cores for analysis of distributed data.
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