I'll take "Hadoop" for $30,000
O’Reilly Media released the results of its second-annual data science salary survey on Thursday (available for free download here), and the results…
Apache Spark is taking the big data world by storm, but the folks at Databricks wanted to disprove a misconception that its only performance advantages over Hadoop MapReduce come in-memory.
There was a lot of news about Spark’s ascension in the big data ranks this week, as well as some speculation. According to Cloudera’s Mike Olson, his company is widely embracing Spark — including to run Hive — but not in place of Impala.
Big data startup Databricks keeps humming along, announcing on Monday a large round of venture capital and a new cloud service that aims to seed adoption of Spark — a framework it says is faster, easier and more versatile than other options.
Databricks, the company behind the commercialization of the Apache Spark data-processing framework, is certifying third-party software to run on the platform. Spark is gaining popularity as a faster, easier alternative to MapReduce in Hadoop environments.
Sprint’s family plan doesn’t let members share data, texts or minutes. Instead they share a collective discount that grows the more members join. Sprint also expanded its new Spark LTE network to six more markets.
Sprint’s(s s) new faster Spark network may be out of reach to most Americans, but it is starting to get more support…
http://www.bigdatarepublic.com/author.asp?section_id=2840&doc_id=269178 This is a pretty interesting benchmark study, although the headline is a bit misleading because Hadoop isn’t really optimized for graph…
Cloudera has partnered with a startup called Databricks to integrate and support the Apache Spark data-processing platform within Cloudera’s Hadoop software. Spark, which is designed for speed and usability, is one of several technologies pushing Hadoop beyond MapReduce.
A team of professors behind the open source Spark and Shark in-memory big data projects has raised $13.9 million to commercialize the products via a company called Databricks. Spark and Shark are designed to be much faster and more flexible than Hadoop MapReduce and Hive.
Hadoop not fast enough for you? Then you might want to get to know AMPLab, a University of California, Berkeley team developing faster versions of many core Hadoop components.