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
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
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.
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.
Skills needed for advanced analytics sometimes require knowledge of recent data-related technologies, analytics techniques and how to use them. However, many organizations realize that this skill set doesn’t negate the need for people who can frame a problem, interpret the output of an analysis and
Discover how two complementary technologies, predictive analytics and prescriptive analytics, can work together to help you forecast future events and choose the best response based on your business objectives.