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.
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.
As part of IBM's ongoing commitment to Hadoop and the broader open source ecosystem, IBM is joining forces with Databricks, Cloudera, Intel and MapR to broaden support for Apache Spark. IBM's goal is to provide enterprise customers with access to the latest innovations around big data and analytics.