By Yolande Poirier-Oracle on May 31, 2016
By Editor in Chief Andrew Binstock
Welcome to the May/June issue of Java Magazine in which we look at how big data is done today. An unusual aspect of the platforms that handle big data is that they almost all run on Java — a testimony to its suitability for enterprise-scale needs.
However, today many of the primary tools don't require enterprise size to be useful. Apache Spark, for example, can work effectively on small, human-size databases. We show how this is done with one developer's project of querying a database of first names to find one for his unborn child. But even if you run Spark in enterprise apps, you'll find that it is much easier to use than earlier tools that required extensive setup and fiddling with systems. Our lead article on Apache 101 shows just how simple it can be to work with big data and produce useful results.
We also look at handling large datasets the traditional ways in two articles: with high-volume JDBC and through enormous in-memory data structures. The latter article shows a clever way of storing tens of gigabytes in memory but off the heap.
For those readers who do unit testing on their code (almost everyone, I trust), we preview the new features coming up in JUnit 5.
The rest of the issue shows off Ceylon, a recently released JVM language from Red Hat; how to set up Java cloud apps; and, for beginners, how generics work in Java — all topped off by our famous language quiz, our no-holds-barred book review, and my editorial. Enjoy!
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