TDWI BIJV20N1 thumbIntersection of technologies that allow us to keep (and work with) data in the cloud.

Business Intelligence Journal | Vol. 20, No. 1

Senior editor Hugh J. Watson examines how the adoption of big data is leading to interest in other data-related approaches and technologies, from data lakes and sandboxes to data labs and data scientists. Big data is having an impact in other ways. Ravi Chandran describes an intersection of technologies that allow us to keep (and work with) data in the cloud.


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bid data managementBig Data Management Platforms:

Architecting Heterogeneous Solutions

Ravi Chandran

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Harvesting the Big Data Tsunami: The Path Beyond Hadoop for Big Data Analytics

By Mike Lamble | October 23, 2012


Today, organizations are awash in Big Data. By “big data” we’re talking about traditional business data such as orders, transactions, customer profiles, as well as new data sources flowing from machines, sensors, and social networks. This is data that is measured in exabytes and beyond. If studies are correct, we may be in for an even greater onslaught because according to IBM, 90% of the world’s digital data has been created in just the last two years.

The waves of big data are massive and arriving continuously, demanding a storage platform that is expansive, economical, and accessible. The massive landing zone requirement is so great that it would have been beyond comprehension only a few years ago.

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Advantages of Software Database Management Systems

by Mike Lamble on November 16, 2012


The data warehousing community has always made room for high performance database management systems (DBMSs) that used proprietary hardware because massive ingest rates and fast response times for big data analytics were not achievable on standard hardware. Now, however, today’s standard x86 hardware, combined with next generation software DBMSs, can deliver the goods at a much lower cost and with many other advantages that are inherent to software running on standard hardware.

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Hadoop Drives Down Costs, Drives Up Usability With SQL Convergence
eweek | Posted 2013-04-22

SPECIAL FEATURE: As more enterprises begin to adopt the Hadoop big data wrangling technology, there is a growing need for SQL convergence.
290x195bigdataanalytics6In 2011, Charles Boicey looked at Twitter, Facebook, Yahoo and other major Web entities and said to himself, "Why do those guys get to have all the fun?"
Boicey, an informatics solutions architect at the UC Irvine Medical Center, said he could very much see that the underlying big data technologies driving the big Web companies could help in the IT environment at the medical center.
Boicey told eWEEK, "I was intrigued by the volume of data and the speed with which they could access it, and I said, 'Why can't we do that' in healthcare?"
"We came to the conclusion that healthcare data although domain specific is structurally not much different than a tweet, Facebook posting or LinkedIn profile and that the environment powering these applications should be able to do the same with health care data," he wrote in a 2012 blog post.

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How Big Databases on Demand Are Paving the Way for Analytics on Demand
Cloud computing plus a new generation of big data analytics DBMS are enabling big databases on demand.

Data has become a pervasive and abundant raw resource that yields competitive advantages. Management wants more, quicker, and deeper insights from increasingly larger data sets. Organizations have to move fast and leverage data in ways never imagined, pose questions that have never been asked, provide answers faster, augment and analyze new data sources in minutes versus months, and experiment with big data sets rather than samples.
Unfortunately, building big databases — from hundreds of gigabytes to hundreds of terabytes or more — typically requires lead times of weeks or months and large capital outlays that often include seven or eight digits. At the bottom of the Maslovian value pyramid of big data analytics is the computing equipment and a database management system (DBMS). Business domain-specific statistical models are at the pyramid's pinnacle, and data warehouses and data marts are somewhere in the middle. Although the data infrastructure adds the least competitive differentiation, it adds as much as 50 percent to the “cost per answer” and “time to answer.”

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