Apache Spark is, without a doubt, the new “big thing” when it comes to big data analytics. Its arrival was highly anticipated by data scientists and other business professionals alike, and its technology has quickly become one of the main resources for businesses looking to analyze big data. Because of its sudden popularity, many people have speculated about whether Apache Spark may be just a passing fad, but the writer of this column from ReadWrite begs to differ, and he has six key reasons why Apache Spark is not just a fleeting moment in big data history.
First of all, Apache Spark’s analytics capabilities are more advanced than other platforms for big data analysis. Spark’s pre-built libraries for machine learning, graph processing engines and tools for accelerated queries all contribute to its sophistication in analytics. Furthermore, despite its sophisticated capabilities, Spark is relatively easy to use compared to Hadoop, Java and MapReduce. With more capabilities and a simpler user interface, it's easy to see why Spark will be sticking around a while. For even more reasons on why Spark is here to stay, continue reading.
Our take: Spark can be even easier to use than this column suggests – Frontline’s Analytic Solver Platform features a built-in link to Apache Spark clusters that makes big data analysis a point-and-click operation -- no R, Java or Python programming required. Read the full article here: 6 Reasons that Apache Spark Isn’t Flickering Out