Teach you how to quickly zero-based big data entry skills
Now is the era of big data, a lot of people want to learn big data, because whether or payroll employment prospects are very good, a lot of people have the transition from other industries to large data industry, the zero-based people who want to learn large how the data to do it? Below zero base to explore how big data skills Quick Start bar.
Many people need to learn is the need for big data has a solid foundation, the programming language is one of the necessary conditions, the current popular programming languages are: Java, Python, PHP, C / C ++, and so on, whether it is learning what door programming language, in short, to a fine grasp of the language is very necessary that we acquire a wide range of Java application talking about oh.
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Java's direction three: JavaSE, JavaEE, JavaME, learning large data, then only need to learn JavaSE on it, in learning Java, we generally need to learn these: basic HTML, CSS, JS, java's, JDBC and database, JSP java web technology, jQuery and AJAX technology, Spring, Mybatis, Hibernate and so on. These courses can help us better understand Java, learn to use Java.
Furthermore, is Linux, big data associated software are basically running on Linux, so in the big data work still needs to learn Linux Oh, and allows you to quickly grasp the big data technologies, but also a great help. Learning shell can well see the script easier to understand and configure large data clusters on future new study out of Big Data technologies will be faster.
For zero-based people to learn big data, no matter what language to learn, combat is very important, so after learning must be timely use of it, the only constant use, will be more experienced, more learned techniques of big data, and more together, I believe you will be able to master this technology.
Big Data Learning Roadmap - make their own system to learn, to know the contents of each stage of learning
Stage a large data base --java language foundations
(1) Java language foundation
Java developers introduced, the familiar Eclipse development tools, Java language foundation, process control Java, Java strings, arrays and Java classes and objects, classes and digital processing core technology, I / O and reflection, multi-threading, Swing programs and collections
（2）HTML、CSS与JavaScript
PC terminal site layout, HTML5 CSS3 basis, WebApp page layout, interactive features developed native JavaScript, Ajax asynchronous interaction, jQuery applications
(3) JavaWeb and databases
Database, JavaWeb development of core, JavaWeb development insider
This stage is for students with no programming basis, or on a solid foundation is not a tutorial, this is very important, just like building a building, which is the foundation, the foundation instability, even if the repair higher, one day came crashing down!
Phase II, Linux & Hadoop ecosystem
Linux system, Hadoop offline computing outline, distributed database Hbase, data warehouse Hive, data migration tool Sqoop, Flume distributed logging framework
This chapter is the foundation courses, to help you enter the big Linux data field to lay the foundation for better learning Hadoop, hbase, NoSQL, Spark, Storm, docker, kvm, openstack and many other courses. Because the companies, without exception, is to use Linux to build or deploy the project.
Courses Hadoop ecosystem, for HDFS architecture and shell as well as a detailed analysis of java operation, from project development to its principles, so that we learn to lay the foundation of big data.
Explain in detail Mapreduce, Mapreduce computing framework can be said to be any of a large company data will be used, as well as each of the data engineers should master's. Hadoop2x set up in front of a cluster lead the development of a large number of MapReduce programs.
Big Data learning course, this total is divided into several stages (end of the article comes with a tutorial)
Phase III, distributed computing framework and Spark & Strom ecosystem
(1) distributed computing frameworks
Python programming language, Scala programming language, Spark big data processing, Spark-Streaming Big Data processing, Spark-Mlib machine learning, Spark-GraphX map calculation, real one: Spark recommendation system (a line of company real project) based combat two : Sina (www.sina.com.cn)
(2) storm system technology architecture
Storm principle and foundation, message queues kafka, Redis tools, zookeeper Detailed, real one: Log warning system project, combat two: you may also like recommendation system combat
Spark large data processing This section covers a comprehensive overview of its programming model Spark ecosystems, research kernel depth. Not only for project developers, even for research Spark students, this part is very meaningful learning curriculum guidelines
Phase IV, big data project combat (first-tier real company project)
Data acquisition, data processing, data analysis, data presentation, data applications
In fact, the project is interspersed with exercises in which the course at the same time to explain the theory of large data, which will be interspersed with practical knowledge to enhance students' understanding and application of big data technology.
Stage 5, big data analytics -AI (Artificial Intelligence)
Data Analyze & prepare the work environment data base analysis, data visualization, Python Machine Learning
Outdoor equipment identification analysis: 1, 2, & neural network image recognition, natural language processing & social network processing, machine learning Python actual project
This stage is the stage to enhance the depth, and lay a good foundation for students who want to switch to artificial intelligence, multi-skill, more greatly enhance the quality of employment.