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.
Apache Spark is at heart an open-source community, but it is going well beyond that identity to also develop into a substantial sector of the analytics market. However, Spark will not be able to achieve its full potential if a robust industry ecosystem does not develop around it.
Something palpable was in the air at Hadoop Summit 2015 that confirmed a new next-big-thing in big data analytics is on the horizon. As this year’s Summit drew to a close, the community enthusiastically looks forward to the emergence of Spark.
Scaling big data analytics applications is expected to become impractical given the rate of increasing volumes, heterogeneous varieties and velocities of data. Continued advances in machine learning are critical to enable data scientists to automatically generate machine learning models for rapidly
Hadoop’s commercial maturation took a big leap forward with the recent establishment of the Open Data Platform (ODP) group, which has created a common interoperability framework. ODP provides users and ISVs with assurances that there is a tested Hadoop core, allowing them to focus on building value
Day two at Hadoop Summit went well beyond the opening day theme of Hadoop’s transformative power for enterprises. The many competing Hadoop ecosystem subprojects in play may be an indication of just how ambiguously Hadoop’s enterprise market boundaries overlap with adjacent segments.
It’s clear that Hadoop is nearing maturity, but if this year’s summit is any indication, this segment remains vibrant and innovative. Indeed, many of the sessions addressed significant gaps in our own knowledge of this fast-moving space.