Open Source Big Data Tools Score High in Quality

An increasing number of organizations are leveraging big data to realize efficiencies in their business processes. They accomplish this by using analytics to track customer behavior and campaign efficiency. With the volume of data being handled by organizations growing exponentially, big data technology is critical for analysis of data that is challenging to deal with as it is often too diverse, fast-changing, or voluminous to address with conventional technology. Also, software quality for projects that handle big data is becoming more important for enterprises to consider.

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Data Centers of the Future

In the very early days of the Internet most servers were stored and cared for at Universities or government installations. Today, however, a whole industry has blossomed with people running data centers and server farms for individuals and companies.

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Driving Business Transformation with Big Data

An introduction to big data

Big data analytics is a technology-enabled strategy for gaining richer, deeper, and more accurate insights into customers, partners, and business operations. This collection of tools, techniques, and technologies ultimately provide competitive advantage and increased agility by deriving insight from complex, large data sets. By processing a steady stream of real-time or static data, organizations can make time-sensitive decisions faster, monitor emerging trends, course-correct rapidly, and jump on new business opportunities.

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CIOs and Big Data – What Your IT Team Wants You To Know

IT teams really wish to help CIOs understand and derive the most benefit from big data. Team players understand that key projects serve an essential purpose in supporting organizational goals. Today across organizations in all industries, most of big data projects never get completed because of various resource issues. /companies too often fail to accurately determine the project’s scope, and leave CIOs and IT departments with inadequate resources and staffing to get the job done. Here is a look at the root causes of most of the Big Data project flops.

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