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
Both robotics equipment and the products produced by such equipment generate large volumes of data useful for ensuring optimal ongoing operation of production processes. Asset analytics for robotic equipment generates several benefits. IBM provides a market-leading analytics platform enabling
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
Google’s recent announcement of Project Brillo, a Weave-based OS for the Internet of Things ecosystem, raises some interesting questions—and some concerns. The OS aims to advance the Internet of Things by enabling connected devices and sensors to speak the same language, but it has other potential
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
In practical terms, what does the fourth Industrial Revolution really mean to industrial manufacturers, and what is actually different now? Here are some examples of how the fourth Industrial Revolution is transforming the manufacturing and industrial landscape.
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
Internet of Things continues to grow by leaps and bounds. Though beneficial for highly innovative applications, all that data broadens the potential for security exposures. Organizations with a vital stake in Internet of Things data need to take data security seriously.
Separating good data from bad and taking advantage of the open source ecosystem offer key advantages for quality analytics and keen insight from valuable data. And two upcoming events offer great opportunities to learn more.
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.