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所在平台: Udemy |
课程主页: https://www.udemy.com/course/real-world-vagrant-build-an-apache-spark-development-env/
课程评论：没有评论
课程名称：现实世界的Vagrant - 构建Apache Spark开发环境！ 课程概述：该课程基于“现实世界的Vagrant用于分布式计算 - Toyin Akin”课程，旨在帮助学员将完整的Spark开发环境打包成自定义的2.3GB Vagrant盒子。完成构建后，学员无需再对Windows机器进行操作，即可运行一个完善的Spark环境。最终解决方案可以在3分钟内启动完整的Apache Spark环境！学员可以安装任何版本的Spark，我们已为1.6.2和2.0.1进行了编码，但很容易扩展到其他版本。 为什么选择Apache Spark？Apache Spark的程序在内存中运行速度比Hadoop MapReduce快100倍，在磁盘上快10倍。它具有先进的DAG执行引擎，支持循环数据流和内存计算。Apache Spark提供超过80种高级操作符，易于构建并行应用程序，并且可以通过Scala、Python和R交互使用。此外，Apache Spark能结合SQL、流处理和复杂分析。它支持一系列库，包括SQL和DataFrames、机器学习的MLlib、图计算的GraphX及Spark Streaming，允许在同一应用程序中无缝结合这些库。 课程大纲：无
Note: This course is built on top of the "Real World Vagrant For Distributed Computing - Toyin Akin" courseThis course enables you to package a complete Spark Development environment into your own custom 2.3GB vagrant box. Once built you no longer need to manipulate your Windows machine in order to get a fully fledged Spark environment to work. With the final solution, you can boot up a complete Apache Spark environment in under 3 minutes!! Install any version of Spark you prefer. We have codified for 1.6.2 or 2.0.1. but it's pretty easy to extend this for a new version. Why Apache Spark. Apache Spark run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.Apache Spark has an advanced DAG execution engine that supports cyclic data flow and in-memory computing.Apache Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python and R shells.Apache Spark can combine SQL, streaming, and complex analytics. Apache Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.
