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There is no need for programming at this stage. Many people have heard of big data, artificial intelligence and data mining. But almost all of them have questions: what is big data? What is artificial intelligence? What can big data and artificial intelligence do? Wait. The main purpose of this stage is to answer questions and questions, so that people can understand these concepts, at least when talking about big data and artificial intelligence with high-end people after dinner. If you already have a thorough understanding of big data and artificial intelligence, you can skip to the first stage.

The first phase: linux system

This chapter is a basic course, to help you enter the field of big data to lay a good foundation for Linux, in order to better learn Hadoop, NOSQL, Oracle, MYSQL, Spark, Storm and many other courses. Because there is no exception in the enterprise is to use Linux to build or deploy projects.

If you want to learn big data well, you'd better join a good learning environment. You can come to this Q Group 251956502 so that it is more convenient for everyone to learn, and you can also communicate and share materials together.

The second stage: high concurrency processing of large websites

Through the study of this chapter, you will understand the source of big data, where the data come from, and then better understand big data. And by learning how to deal with the high concurrency problem of large websites, we can learn more deeply about Linux and explore the architecture from a higher point of view.

The third phase: Hadoop distributed file system: HDFS

This stage is to enter "big data" an entrance, need to master the basic principles of HDFS, know why it can store huge amounts of data, know "Baidu network disk" itself is what? Can you realize a network disk by yourself? Let everyone enter the actual combat state of big data from the very beginning.

The fourth phase: Hadoop distributed computing framework: Mapreduce

This stage focuses on the implementation of the principle of MR, case application as the main line, with source code analysis to enable students to understand more clearly what is distributed computing, computing parallelism, computing to data movement, computing localization data reading and so on.

The fifth stage: Hadoop offline system: Hive

This stage introduces that Hive is a data warehouse tool based on Hadoop, which can map structured data files to a database table, and provide a simple sql query function, which can transform sql statements into MapReduce tasks to run. Its advantage is that the learning cost is low, simple MapReduce statistics can be quickly realized through SQL-like statements, and there is no need to develop special MapReduce applications, so it is very suitable for statistical analysis of data warehouse.

There are 16 stages, the other stages are shown in the figure

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