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.
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 arguably surpassing Apache Hadoop as the preferred big data analytics development platform. Yet, the expected specialized algorithm and model libraries that emerge from the Spark community raise the specter of platform bloat that may perhaps put Spark at risk of becoming too bloated
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.
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.
Many machine-learning-as-a-service (MLaaS) offerings have natural language processing at their core to support applications such as concept expansion, language identification, machine translation, user modeling, visualization rendering and more. Discover how MLaaS boosts productivity by uncovering
Watson solutions aim to enhance, scale and accelerate human expertise, targeting a wide range of complex challenges in the world today. Watson Analytics itself will understand, perceive and relate information at a human level for your business problems, combining analytical insights with your
Machine learning is a tool for making humans more productive. Machine learning methods vary in degrees of automation. Regardless of how one performs machine learning, data scientists must still prepare the data sets, specify the algorithms, execute them and interpret the results. The process of