The in-memory batch-processing framework sheds more JVM performance bottlenecks as a major Hadoop vendor eyes Spark as a full-blown replacement for the aging MapReduce Apache Spark, the in-memory data ...

As analytics accelerate closer to real-time, historical analytics are not being displaced. The benefits of a comprehensive and historic view of data is becoming more than just a daydream. Imagine a ...

Apache Spark, the in-memory and real-time data processing framework for Hadoop, turned heads and opened eyes after version 1.0 debuted. The feature changes in 1.2 show Spark working not only to ...

Apache Spark is an execution engine that broadens the type of computing workloads Hadoop can handle, while also tuning the performance of the big data framework. Hadoop specialist Cloudera recently ...

While MapReduce still enjoys widespread use in the Hadoop ecosystem, the number of new deployments that are being brought online is declining. And the trend has not gone unnoticed by the vendors that ...

Clusters must be tuned properly to run memory-intensive systems like Spark, H2O, and Impala alongside traditional MapReduce jobs. This Hadoop Summit 2015 talk describes Altiscale’s experience running ...

The first Spark Summit East conference concluded yesterday, just a month after Apache Spark practically stole the show at the Strata+Hadoop World conference, reinvigorating the debate about where the ...

Why is Spark So Hot? The amount of data generated around the globe each day is 2.5 exabytes (Adepta, March 2015), and the big data market reached $27.4 billion in 2014 (Wikibon, March 2015). Spark is ...

There is more to big data than Hadoop, but the trend is hard to imagine without it. Its distributed file system (HDFS) is helping businesses to store unstructured data in vast volumes at speed, on ...

June was an exciting month for Apache Spark. At Hadoop Summit San Jose, it was a frequent topic of conversation, as well as the subject of many session presentations. On June 15, IBM announced plans ...