A panel of experts agree that big data has fantastic potential, but successes, at least at this point, are often hit or miss.
The new 5.0 release of the VoltDB in-memory database simplifies fast data application development.
The collection and analysis of big data workloads was perhaps the biggest trend in enterprise computing during the last 12 months, yet the lasting impact of these projects at many companies won't be fully realized for months—even years—to come. Still, market evidence suggests that in the next 12 months, big data analytics will be matriculating beyond first-movers and going mainstream. IDC recently documented these predictions in " IDC FutureScape: Worldwide Big Data and Analytics Predictions for 2015." The research firm cited the "digitization of everything," the continued increase in the number of data producers and new expectations for information access as key drivers for the coming year. While these are undoubtedly major trends for 2015, Chris Surdak, a global subject matter expert at HP Software, said they only set the stage for what his company expects in the market. In this slide show, developed with eWEEK reporting and input from Surdak, we share key trends for the coming year in the big data space.
A Teradata study indicates that cultural gaps between CEOs and middle managers can hinder companies' success on big data and analytics efforts.
Oracle's pricing drop is parallel to something one might see at a big box store -- only for big computing devices that go for hundreds of thousands of dollars.
Apache Spark is a rapidly evolving open-source engine for large-scale data processing and analytics. In development for a number of years at UC Berkeley's AmpLab, it is now being driven by Databricks, a Berkeley spin-out founded by Ion Stoica and Matei Zaharia. It is also reaching a level of maturity that moves it beyond pure experimentation—with imminent availability of a stable 1.0 release and inclusion, current or planned, in all major Hadoop distributions. There's good reason for all of the interest. Spark accelerates analytics on Hadoop, working as a full suite of complementary tools, including a fully featured machine learning library (MLlib), a graph processing engine (GraphX) and stream processing. Spark can access data in a variety of sources, including HDFS, Cassandra and HBase. The following eWEEK slide show, based on our own reporting and input from Peter Schlampp, vice president of product at Platfora, shares the different reasons some say Spark is the best thing to happen to data.
The so-called "digital universe"—all the data generated digitally around the globe—is expanding at a breakneck pace. By 2020, researchers predict that the ongoing onslaught of data will catapult to 44 zettabyes—up from the current 4.4 zettabytes. No industry (or function within an industry) will be immune to the data deluge, and data centers across virtually every industry will feel the pressure first. Concerns surrounding efficiency, performance and cost are already mounting. To extract meaningful value from big data, you need optimal processing power, analytics capabilities and skills. IBM is helping organizations across industries regain control of their data centers and meet, if not exceed, intensifying demands for fast access to data, more efficient data management and easier scalability. Here are examples of how IBM has teamed up with leading organizations in sports, health care and education to help manage how they store, analyze, apply and share their large and quickly growing amounts of data.
Overall cloud-related revenue was up 47 percent year over year, a metric that is very important to the long-term health of the company.
Data is a new form of capital. Ultimately, information about people, places and things will truly differentiate enterprises.
With many veterans suffering from posttraumatic stress disorder (PTSD), the U.S. Department of Veterans Affairs is looking to IBM's Watson for help.
Streams of data are added up and analyzed to offer indications as to whether a potential customer really intends to buy the product that's for sale.
The Apache Software Foundation graduated its MetaModel and Drill projects from the organization's incubator to become Top-Level Projects.
MicroStrategy ships a biometrically secured analytics solution to the market with support for Apple Touch ID.
Enterprises of all types and sizes are realizing that data sets being stored or archived in silos or in clouds—information they might have had considered too old or irrelevant, or only for regulation purposes—may have great potential value. It's all about looking at a business' history, making cogent queries, discovering insights and projecting what is likely to happen in the future, in order to become more customer-centric and inventory-effective. These companies are going into the internal business of analyzing data. As a result, organizations are in search of the necessary tools and information to take full advantage of the potential this movement offers. However, big data brings big hype, and big hype only brings big confusion of what's what in the data market. In this slide show, eWEEK and Gary Nakamura, CEO of data application infrastructure provider Concurrent, discuss—and dismiss—the biggest myths that are disrupting the big data industry. Some of what turns out to be a myth may surprise you.
After an early beta of IBM's Watson Analytics pulled in 22,000 early testers, Big Blue is now opening the service to everybody.