Data scientists may be of a different breed from other analytics team members, but they are essential for bringing to the table curiosity about data and an unquenchable thirst for finding patterns and relationships in that data. Discover how combining the roles of data scientist, business analyst,
As one principle of the buffalo theory demonstrates, open source projects are applying a process of natural selection through the manner in which they tackle performance bottlenecks and other obstacles that can prevent further technological advancement. By continually identifying and addressing the
Big data has shown itself to be an illuminating force for sourcing the insight that is powering a tremendous transformation in modern life. To keep pace with the rapid changes, today’s organizations are seeking to improve their capabilities, competencies and culture to turn data into business value
While some observers may argue that Apache Spark is causing the relevance of the Apache Hadoop community to wane, the fact of the matter is innovative Spark development depends on Hadoop platforms. Discover why Hadoop is stronger than ever as an open source information refinery that is expected to
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
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.
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
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.
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
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.