Big Data Management Platforms:
Architecting Heterogeneous Solutions
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.
Hadoop Drives Down Costs, Drives Up Usability With SQL Convergence
SPECIAL FEATURE: As more enterprises begin to adopt the Hadoop big data wrangling technology, there is a growing need for SQL convergence.
In 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.
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.”