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This article mainly explains "how to learn hadoop". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how to learn hadoop.

There are two steps in Hadoop learning: linux learning and hadoop learning.

I have a java foundation before I come into contact with hadoop, so my plan is to learn Linux first, and then to learn the hadoop ecosystem. To learn hadoop*** step-by-step plan is to learn linux, and then linux can be divided into four steps.

The goal of the linux task is to be proficient in operating linux commands and network configuration.

The second goal is to be familiar with user and rights management operations.

The third goal is to be familiar with software packages and system command management, and the fourth is to learn shell programming.

After the completion of the study of the Linux system, the second one plans to learn Hadoop.

The Hadoop phase is divided into two steps.

* I am proficient in building pseudo-distributed clusters and completely distributed clusters. I personally think it is better to build a hadoop environment before learning hadoop. Just like looking at other people's code, we first put other people's code to run, and then analyze the code, if the code can not run, how to talk about code analysis, so first let the hadoop environment set up, can run wordcount programs normally, we can slowly analyze the entire Hadoop ecosystem, so that we can have an overall understanding of hadoop.

The second stage of Hadoop is subdivided into several stages based on the * * stage:

First of all, get to know the hadoop ecosystem as a whole, understand the MapReduce distributed computing framework, Yarn cluster resource management and scheduling platform, hdfs distributed file system, hive data warehouse, HBase real-time distributed database, Flume log collection tools, sqoop database ETL tools, zookeeper distributed collaboration services, Mahout data mining libraries

In the second stage, it is easy to learn MapReduce,MapReduce as the core content of hadoop, first understand Mapper, Reducer, Configuration, job and other classes, be familiar with MapReduce flow chart, then write simple word statistics code, understand MapReduce as a whole, and then learn MapReduce operating mechanism, proficiency, MapReduce input format, MapReduce output format, and MapReduce optimization.

The third stage of learning hadoop another core content HDFS, first understand what is hdfs, and then analyze the advantages of hdfs, and then understand the shortcomings of Hdfs, HDFS is how to store data, what kind of architecture, how we achieve read and write files in HDFS, as well as understand HDFS copy storage strategy, while proficient in HDFS shell access and java access.

The third stage is to learn another core content of hadoop: Yarn. First of all, we need to understand what Yarn is, why we use the architecture of Yarn,Yarn, and the principle of Yarn. I personally think that as long as the beginners of hadoop have mastered the HDFS that provides massive data storage, the MapReduce of distributed computing, and the yarn of the cluster resource management and scheduling platform, they have basically mastered the core of Hadoop and laid a solid foundation for future hbase and hive learning.

At this point, I believe you have a deeper understanding of "how to learn hadoop". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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