The data science revolution is bringing a shift in the direction of the cloud—and with it a sea change in the roles of IT professionals. Discover how focusing on data science can help you keep your career afloat in a changing business environment.
Why are people talking about Apache Spark? It’s because many organizations are using the myriad features of this open source engine to boost their predictive analytics processing. The result? Better, deeper and faster data analyses with reduced coding time and effort.
Advanced analytics can boost organizational decision-making and offer multiple benefits including increased revenue and decreased costs. However, achieving these goals requires an emphasis on three strategic pillars: focusing on business decisions, embracing an agile culture and investing in an
Inaccurate perceptions of predictive analytics are common in the business world. In reality, predictive analytics is straightforward to understand, can leverage existing skillsets in business and IT organizations, and can deliver value in most industries and lines of business. Getting started with
Many students at today’s colleges and universities balance their studies with full-time employment. Reflecting on her own daughter’s pressures from attending school and working a full-time job, Jen considers the innovative use of advanced analytics used by some institutions of higher learning to
IBM is investing deeply in Spark in a wide range of long-term initiatives. Discover how IBM’s long history of joining powerful, innovative open-source projects allows it to create markets by contributing significant technological improvements and supporting business solutions.
An open-source software platform called Apache Spark is growing rapidly in popularity as an essential platform for rapidly modeling, exploring and analyzing data. Here are nine reasons why developers and data scientists are primed to #SparkInsight with Spark.
On Tuesday, I plunged right back into Spark Summit—which, if anything, was buzzing more vigorously with interesting content than it had been the day before. Not surprisingly, IBM’s Spark announcements were the talk of the show.
A growing body of fresh thinking is coming down the pike. Much of it will come from the droves of IBMer data scientists who participated in the recent and wildly successful internal Hack Spark Challenge, as well as ongoing IBM-sponsored hackathons, meetups and developer days focusing on Spark.
Speed seems to always be at least one of the key factors in the evolution of any technology. The in-memory, real-time processing capability of Spark is rapidly advancing fast-cycle big data processing that supports a broad range of workloads.
IBM made several, significant announcements signaling its commitment to providing an open, mature, innovative industry Apache Spark ecosystem to accelerate its adoption. Take a detailed look at why IBM is making a huge, strategic bet on Spark.