Everybody talks about the weather—and now businesses can turn that talk into insight. A recent hackathon highlighted ways data scientists can create smart data applications for meteorological forecasting and address the weather data analytics needs of a range of organizations.
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.
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
Apache Spark is gaining considerable notice in the data science community, and the technology was showcased in the recent debut of a Spark hackathon series. Take a look at a web server enabling Spark cloud instances to serve as web end points and an application to predict stock movement that were
Apache Spark is unfamiliar to many data analytics professionals. A recent post provides high-level guidance on how they might begin to identify the applications for which Spark is well suited. This post expands on that discussion to offer further details for triggering the creative imaginations of
Get in on the widespread excitement over Apache Spark. Check out the highlights from a recent SparkInsight CrowdChat that tackled six key questions about this next-generation, cluster-computing, runtime processing environment and development framework for in-memory processing of advanced analytics.
Apache Spark is a next-generation, cluster-computing, runtime processing environment and development framework for in-memory advanced analytics. This presentation provides a crisp, ten-point summary of what every data analytics professional should know about Spark.
An increasing number of use cases for big data and analytics can be Apache Spark's sweet spots. Take a look at several low-latency applications in which Spark is well-suited for analysis of cached, live data.
The drive toward industry openness continues at full speed, and Apache Spark is expected to become one of the centerpieces of the big data industry fabric. As a closely aligned technology with Apache Hadoop, it stands to benefit from broad adoption of core open data platform technologies.
Poised for widespread commercial adoption, Apache Spark is drawing a lot of attention with its ability to perform advanced in-memory analysis of cached, unstructured data in an open source distributed-computing framework.