Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, I

First Page		Document Content
Date: 2015-01-05 06:37:34 Computing Hadoop Apache Software Foundation Parallel computing Apache Spark Cluster computing Java platform Apache Hadoop Data-intensive computing MapReduce Apache HBase PageRank		Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, I Add to Reading List Source URL: nil.csail.mit.edu Download Document from Source Website File Size: 865,54 KB Share Document on Facebook

	Databricks Security A Primer About Databricks Databricks is a hosted end-to-end data platform powered by Apache® Spark™. Databricks makes it easy to DocID: 1uwO3 - View Document
	Dublin Apache Kafka Meetup, 30 AugustThe SMACK Stack: Spark, Mesos, Akka, Cassandra, Kafka Elizabeth K. Joseph DocID: 1tHnJ - View Document
	Peeling the Onion How Data Abstractions Help Build BigData Apps Andreas Neumann @caskoid November 2016 Cask, CDAP, Cask Hydrator and Cask Tracker are trademarks or registered trademarks of Cask Data. Apache Spark, Spark, DocID: 1tdJO - View Document
	We explore the trade-offs of performing linear algebra in Apache Spark versus the traditional C and MPI approach by examining three widely-used matrix factorizations: NMF (for physical plausibility), PCA (for its ubiquit DocID: 1sokL - View Document
	Towards a Big Data Debugger in Apache Spark Tyson Condie, UCLA Tuning Spark Applica>ons •  Commonly through visualiza>on tools DocID: 1s5yq - View Document