大数据基础到精通完整版， 涵盖技术点：python 基础 java基础，mysql,oracle,ssm框架，linux,hadoop,hbase,zookeeper,flume,scala,spark。资源宝贵，速度下载

全国2014-2018年空气质量csv数据集文件数据，包含字段time（时间），city（城市），AQI，PM2.5，PM10，SO2，NO2，CO，O3，primary_pollutant（主要污染物），共计55万条数据。

Scala是一门多范式的编程语言，一种类似java的编程语言，设计初衷是实现可伸缩的语言、并集成面向对象编程和函数式编程的各种特性。 此为安装程序

Apache Spark 是专为大规模数据处理而设计的快速通用的计算引擎。Spark是UC Berkeley AMP lab (加州大学伯克利分校的AMP实验室)所开源的类Hadoop MapReduce的通用并行框架，Spark，拥有Hadoop MapReduce所具有的优点；但不同于MapReduce的是——Job中间输出结果可以保存在内存中，从而不再需要读写HDFS，因此Spark能更好地适用于数据挖掘与机器学习等需要迭代的MapReduce的算法。

大数据领域必读！此文件为第二分卷。

大数据领域的必读书！此文件为第一分卷。

linux中搭建spark环境使用的spark-1.6.0-bin-hadoop2.6.tgz安装包

里面包含很多spark源码（包括etl,kafka,hbase整合等）

spark-assembly-1.6.1-hadoop2.6.0.jar,欢迎下载，，，，，，，，，，，，，，，，，，，，

python爬虫爬取股票评论， 调用百度AI进行语义分析， matlab数据处理， excel作图 股票涨跌和评论的关系

Spark入门完整版 PDF版，从生态圈 部署安装 编程模型 运行框架 stream sql mllib graphx tachyon安装部署介绍

Spark_GraphX大规模图计算和图挖掘

Spark 入门实战系列，适合初学者，文档包括十部分内容，质量很好，为了感谢文档作者，也为了帮助更多的人入门，传播作者的心血，特此友情转贴: 1.Spark及其生态圈简介.pdf 2.Spark编译与部署（上）--基础环境搭建.pdf 2.Spark编译与部署（下）--Spark编译安装.pdf 2.Spark编译与部署（中）--Hadoop编译安装.pdf 3.Spark编程模型（上）--概念及SparkShell实战.pdf 3.Spark编程模型（下）--IDEA搭建及实战.pdf 4.Spark运行架构.pdf 5.Hive（上）--Hive介绍及部署.pdf 5.Hive（下）--Hive实战.pdf 6.SparkSQL（上）--SparkSQL简介.pdf 6.SparkSQL（下）--Spark实战应用.pdf 6.SparkSQL（中）--深入了解运行计划及调优.pdf 7.SparkStreaming（上）--SparkStreaming原理介绍.pdf 7.SparkStreaming（下）--SparkStreaming实战.pdf 8.SparkMLlib（上）--机器学习及SparkMLlib简介.pdf 8.SparkMLlib（下）--SparkMLlib实战.pdf 9.SparkGraphX介绍及实例.pdf 10.分布式内存文件系统Tachyon介绍及安装部署.pdf

该scala项目时自己整理的，对初学者很有帮助，里面包含了scala各种技术

官网下载的 https://www.scala-lang.org/download/，放心使用

当在windows开发hadoop时，需要配置HADOOP_HOME环境变量，变量值D:\Program Files\hadoop-common-2.7.3-bin-master，出现如下错误之一，需要配置： 1、org.apache.hadoop.io.nativeio.NativeIO$Windows.createDirectoryWithMode0(Ljava/lang/String;I)V 2、org.apache.hadoop.io.nativeio.NativeIO$Windows.createDirectoryWithMode0

大数据项目源代码,电影推荐系统Movie_recommend-master,包括实时推荐和离线推荐

亚马逊美食评论50万数据集(Amazon Fine Food Reviews) 之前做评论分类 好不容易找到的数据集 分享了。 需要更多资源请关注。 Github: https://github.com/huangyueranbbc

netcat被誉为网络安全界的‘瑞士军刀'，相信没有什么人不认识它吧...... 一个简单而有用的工具，透过使用TCP或UDP协议的网络连接去读写数据。它被设计成一个稳定的后门工具， 能够直接由其它程序和脚本轻松驱动。

spark-2.1.0-bin-hadoop2.7.tgz linux 安装文件 。

基于spark、hadoop、kafka、MongoDB、flume、elasticseach、angular等大数据框架实现实时+离线推荐，文件里面是源代码，还附送教学视频，让零基础的新手也能快速实现该系统，从而获得一个拿得出手的项目，帮助大家转型大数据或找工作等。

Learning Apache Spark 2 by Muhammad Asif Abbasi English | 6 Jun. 2017 | ASIN: B01M7RO7US | 356 Pages | AZW3 | 16.22 MB Key Features Exclusive guide that covers how to get up and running with fast data processing using Apache Spark Explore and exploit various possibilities with Apache Spark using real-world use cases in this book Want to perform efficient data processing at real time? This book will be your one-stop solution. Book Description Spark juggernaut keeps on rolling and getting more and more momentum each day. The core challenge are they key capabilities in Spark (Spark SQL, Spark Streaming, Spark ML, Spark R, Graph X) etc. Having understood the key capabilities, it is important to understand how Spark can be used, in terms of being installed as a Standalone framework or as a part of existing Hadoop installation and configuring with Yarn and Mesos. The next part of the journey after installation is using key components, APIs, Clustering, machine learning APIs, data pipelines, parallel programming. It is important to understand why each framework component is key, how widely it is being used, its stability and pertinent use cases. Once we understand the individual components, we will take a couple of real life advanced analytics examples like: Building a Recommendation system Predicting customer churn The objective of these real life examples is to give the reader confidence of using Spark for real-world problems. What you will learn Overview Big Data Analytics and its importance for organizations and data professionals. Delve into Spark to see how it is different from existing processing platforms Understand the intricacies of various file formats, and how to process them with Apache Spark. Realize how to deploy Spark with YARN, MESOS or a Stand-alone cluster manager. Learn the concepts of Spark SQL, SchemaRDD, Caching, Spark UDFs and working with Hive and Parquet file formats Understand the architecture of Spark MLLib while discussing some of the off-the-shelf algorithms that come with Spark. Introduce yourself to SparkR and walk through the details of data munging including selecting, aggregating and grouping data using R studio. Walk through the importance of Graph computation and the graph processing systems available in the market Check the real world example of Spark by building a recommendation engine with Spark using collaborative filtering Use a telco data set, to predict customer churn using Regression About the Author Asif Abbasi has worked in the industry for over 15 years, in a variety of roles starting from engineering solutions to selling solutions and everything in between. Asif is currently working with SAS a Market Leader in Analytic Solutions as a Principal Business Solutions Manager for the Global Technologies Practice. Based out of London, Asif has vast experience in consulting for major organizations & industries across the globe, and running proof-of-concepts across various industries including but not limited to Telecommunications, Manufacturing, Retail, Finance, Services, Utilities and Government. Asif has presented at various conferences and delivered workshops on topics such as Big Data, Hadoop, Teradata, and Analytics using Aster on Teradata and Hadoop. Asif is a Oracle Certified Java EE 5 Enterprise Architect, Teradata Certified Master, PMP, Hortonworks Hadoop Certified developer and Administrator. Asif also holds a Masters degree in Computer Science and Business Administration.

Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark by Russell Jurney English | 7 Jun. 2017 | ASIN: B072MKL34K | 352 Pages | AZW3 | 5.91 MB Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track

idea的scala插件，解压缩后吧scala文件夹放到idea安装文件夹下的plugins文件夹中即可。idea只有安装该插件后才可以使用scala编程

根据吕琳媛、周涛《链路预测》中的相似性指标计算示例，参考附录提供的代码分别用python和matlab实现CN、Jaccard、RA指标的计算。并用书中提供了简单的五点的无权无向网络进行测试，结果与作者的计算吻合。 内含文件如下： （1）书中相似性指标计算示例原文 （2）网络的邻接表.txt文件 （3）matlab代码 （4）python代码 （5）python生成的网络图

scala2.10.6，官网下载贼慢 scala2.10.6，官网下载贼慢 scala2.10.6，官网下载贼慢 scala2.10.6，官网下载贼慢 scala2.10.6，官网下载贼慢

资源包含《Scala编程》第3版英文版和第3版源代码，Scala编程第3版，目前是最新版，支持Scala 2.11以上。《Scala编程》是Scala语言的创始人参与编写的，涵盖的语法特性非常全面，并且作者解释了为什么这么设计，有怎样的考虑，开发者应该以什么样的方式使用。 因此学习这本书，不单单是学会了scala语言本身，更重要的是扩展了视野，提升了思维能力和培养了考虑问题的思维方式方法。

一个完成的spark mllib 协同过滤推荐算法ALS 完整实例程序，基于 spark yarn-client模式运行，另外，包括训练数据。

Frank Kane's Taming Big Data with Apache Spark and Python English | 2017 | ISBN-10: 1787287947 | 296 pages | AZW3/PDF/EPUB (conv) | 6.12 Mb Key Features Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with Spark Book Description Frank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. What you will learn Find out how you can identify Big Data problems as Spark problems Install and run Apache Spark on your computer or on a cluster Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets Implement machine learning on Spark using the MLlib library Process continuous streams of data in real time using the Spark streaming module Perform complex network analysis using Spark's GraphX library Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster About the Author My name is Frank Kane. I spent nine years at Amazon and IMDb, wrangling millions of customer ratings and customer transactions to produce things such as personalized recommendations for movies and products and "people who bought this also bought." I tell you, I wish we had Apache Spark back then, when I spent years trying to solve these problems there. I hold 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, I left to start my own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. Table of Contents Getting Started with Spark Spark Basics and Simple Examples Advanced Examples of Spark Programs Running Spark on a Cluster SparkSQL, Dataframes and Datasets Other Spark Technologies and Libraries Where to Go From Here? - Learning More About Spark and Data Science

本书一共11章：其中第1～3章，主要介绍了Spark的基本概念、编程模型、开发与部署的方法；第4～11章，详细详解了热点新闻分析系统、基于云平台的日志数据分析、情感分析系统、搜索引擎链接分析系统等的应用与算法等核心知识点。

慕课网Spark SQL 入门到精通到项目实战的世界（全套日志分析）日志文件

基于SPARK的大数据实战（在线电影推荐），使用最主流的大数据技术，实现电影的推荐，包含代码。

全国2014-2018年空气质量csv数据集文件数据，包含字段time（时间），city（城市），AQI，PM2.5，PM10，SO2，NO2，CO，O3，primary_pollutant（主要污染物），共计55万条数据。

基于 Flume+Kafka+Spark Streaming 实现实时监控输出日志的报警系统的 Spark Streaming 程序代码，博客链接： https://blog.csdn.net/linge1995/article/details/81326146

本数据是淘宝开源的用户真是产生的数据，包括商品id，用户id，商品品类id，生成时间，用户行为:pv,cut(加入购车车)。。。。

这个里面装的是本人写的一个关于广告精准投放的一个spark项目，实现语言的是scala，然后里面有代码和注释，还有需要的操作的日志文件，东西很齐全