Want to learn from big data? This is the complete big data learning system.

The practical information starts, and there is no more gossip. Here are the learning ideas of big data sorted out by the editor.

The first phase: linux system

This stage is for big data to learn the basic course of introduction, to help you get into big data and lay a good foundation for Linux, so as to better learn many technical points, such as Hadoop, habse, NoSQL, saprk, storm and so on.

Another: there is no doubt that the exception in the enterprise is to use Linux to build or deploy projects.

Here I still want to recommend the big data Learning Exchange Group I built myself: 529867072, all of them are developed by big data. If you are studying big data, the editor welcomes you to join us. Everyone is a software development party. Irregularly share practical information (only related to big data software development), including the latest big data advanced materials and advanced development tutorials sorted out by myself. Welcome to join us if you want to go deep into big data.

The second stage: high concurrency processing of large websites

The purpose of this stage of study is to enable you to understand the source of big data, data, and then a better understanding of big data. By learning to deal with the high concurrency of large websites and reverse more in-depth study of Linux, colleagues stand from a higher point of view to explore the architecture.

The third stage: Hadoop learning

1. Hadoop distributed file system: HDFS

Dissect HDFS in detail, understand its working principle, and lay a good foundation for learning from big data.

2. Hadoop distributed computing framework: MapReduce

MapReduce can be said to be a computing framework that any big data company will use, and it is also a computing framework that every big data engineer should master skillfully.

3. Hadoop offline system: Hive

Hive is a Hadoop framework that uses SQL for careful computing. It is often used in work, and it is also the focus of face-to-face teaching.

4. Hadoop offline computing system: HBASE

The importance of HBASE is self-evident. Even engineer big data, who has worked for many years, needs to focus on HBASE performance optimization.

Phase IV: zookeeper development

Zookeeper plays a more and more prominent role in distributed clusters, and it also provides great convenience for the development of distributed applications. When learning zookeeper, we mainly learn the depth of zookeeper, client development, daily operation and maintenance, web interface monitoring and so on. Learning the content of this part is also very important to the study of the following technology.

Phase 5: elasticsearch distributed search

Phase 6: CDH cluster management

Phase 7: storm real-time data processing

This stage covers the internal mechanisms and principles of storm, mastering everything from data collection to real-time extremes to data storage to foreground presentation. One person says that all the work is completed and the knowledge covers a wide range of knowledge.

Phase 8: Redis cache database

Do a full study of Redis, including its characteristics, hash set types, string types, etc., and finally to optimization, do a detailed study

Phase 9: core part of spark

This stage covers the overview of spark ecosystem and its programming model, in-depth research on the kernel, the principle and practice of Spark on Yarn,Spark Streaming streaming computing, the multilingual programming of Spark SQL,Spark and the principle and operation of SparkR.

After understanding the above knowledge points, the part of cloud computing machine learning is also very important. Usually in the part of cloud computing, we will understand and learn about Docker, virtualized KVM and cloud platform OpenStack to prevent meeting in our future work.