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.
The current Insight Economy demands a never-ending stream of data-driven discovery. Enter the data scientist—professionals who answer this demand by tapping into big data analytics to uncover emerging trends that help transform organizations.
When customers or other key stakeholders expect to be able to connect with an organization instantaneously, extremely low latency, high throughput data and analytics flows and execution are absolutely essential. The advent of the Internet of Things is among several key drivers of the emergence of
Streaming analytics is becoming ubiquitous as data streams from a range of sources, including the Internet of Things, are now mainstream. Although streaming analytics is not a new technology, it is well suited for today’s real-time, low-latency business and consumer applications. And today’s data
Time series data can contain highly valuable insights—if organizations can detect and classify the events within it. An approach that combines stream processing and machine learning holds the key to analyzing large, fast data streams.
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.
Combining data, design and speed, IBM and Spark are creating a new blueprint of innovation together. This is the start of something big. IBM and Spark - Power of data. Simplicity of design. Speed of innovation.
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.