A document classification model can join together with text analytics to categorize documents dynamically, determining their value and sending them for further processing. Learn how a quick, efficient solution can create business advantage.
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
An ever-changing business environment is forcing data professionals to rethink their work methods—but fortunately, help is available. Here are five trends that are making life easier for data professionals: the emergence of Apache Spark, opportunities for greater skill reuse, growth in online and
Using Apache Spark, we built an end-to-end fingerprinting tool to identify matching candidates among two independent data sets, calculating a similarity score and solving the stable marriage problem. Integration with a graphical environment not only saved us time, but also allowed us to easily
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.
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.