Oracle is looking more and more like Salesforce every day. This is a direction the company needs to follow as its hardware sales continue to slip.
The company now is moving into agile, fast-iterative and specialized software development for apps using its HANA in-memory database.
Percona Server for MongoDB has been downloaded by more than 30,000 users and 3,000 enterprises since its initial launch last fall.
After a decade of proving that Hadoop is not just hype, much of the focus and attention of this open-source community now goes to its evolving ecosystem of tools and applications—Spark, Impala, Hive—that are helping usher in new users exploring new use cases. However, as additional workloads are added to a cluster, the challenges of using Hadoop in production grow exponentially and become increasingly complicated. How will clusters react to massive growth and unpredictable changes in usage? Your cluster may be operating just fine right now, but what happens in a few months as you add hundreds of new workloads? When a cluster has hundreds of nodes each running dozens of jobs, Hadoop quickly becomes a chaotic system, and when uses are constantly and dynamically changing, it has an impact on business-critical performance. Ultimately, Hadoop won't move forward into the next decade unless the community addresses one massively important and consistently overlooked thing: quality of service (QoS). This eWEEK slide show explores issues around QoS for Hadoop.
Bear Naked is using IBM's Chef Watson "cognitive cooking" technology to enable consumers to create customized granola.
While CRM software and applications like payroll and expense reporting have moved steadily toward the cloud, business intelligence (BI) and big data analytics have been slower to follow the lead. But as the cloud becomes more mainstream, all signs point to "go" for analytics to get out of the shadows and step into the cloud. Research firm Forrester predicts that by mid-2016 nearly three-quarters of companies will use cloud-based BI. While on-premise analytics deployments will continue for the foreseeable future, the tides are changing when it comes to organizational comfort with moving business-critical functions like BI and analytics to the cloud. Whether the decision to move to the cloud is instigated by economics or the ever-increasing speed of business, organizations need to become data-driven faster, and turning to the cloud sooner rather than later will help get them there. eWEEK recently spoke with Stefan Groschupf, founder and CEO of big data analytics provider Datameer, to glean six reasons why now is the prime time for BI and analytics to step into the cloud spotlight.
The latest version of the software includes making it easier for nontechnical business-line employees to run analytics initiatives.
The DataStax Enterprise Graph scalable, real-time graph database powers cloud apps that manage complex and highly connected data.
IBM and the American Cancer Society are working on a virtual cancer health advisor to provide personalized guidance to cancer patients.
LinkedIn has open-sourced Dr. Elephant, its tool to improve developer productivity and cluster efficiency by making it easier to tune Hadoop jobs.
IBM's new z/OS Platform for Apache Spark gives data scientists and developers real-time access to mainframe data for faster analysis.
The latest release candidate makes it easier for customers to incorporate R-enabled analytics into their database setups.
Adobe adds a series of new features to its Target personalization solution, including a new Lifetime Value (LTV) algorithm and analytics integrations.
A Cloud Machine runs the same APIs as the Oracle public cloud, enabling users to run what amounts to a virtual private cloud inside a firewall in data centers.
Microsoft's mission is to empower everyone to achieve more, and two of the most transformative trends affecting business today are the Internet of things (IoT) and big data. Today's enterprises rely on a constellation of mobile devices, software as a service and billions of connected "things" outputting astonishing quantities of data. By connecting devices and assets they already own, enterprises not only capture rich data, but also harvest the raw material that cloud-powered machine learning, perceptual intelligence and analytics distill into valuable insights to transform their business. Microsoft offers the infrastructure for uncovering those valuable insights and making them actionable. Aiming to help companies make better decisions faster, Microsoft put years of research into its Cortana Analytics and Azure IoT suites. With Microsoft's Build conference coming up next week in San Francisco, this eWEEK slide show highlights six Microsoft customers and illustrates how connecting devices and assets they already had with IoT and the cloud unlock new actionable insights, forecasts and trends from their data.