Want to get into big data? You must plan the learning route

? ? ? ? The following are the big data learning ideas compiled by Alibaba Cloud.

Stage 1: Linux

This phase provides basic courses for Big Data learning, helping you get started with big data and lay a good foundation for Linux, so as to better learn hadoop, habse, nosql, saprk, storm, and many other technical points.

Another exception is the use of Linux to build or deploy projects.
Stage 2: High concurrency processing for large websites

This phase of learning aims to allow everyone to understand the source and data of big data, and then better understand big data. By studying and handling the high concurrency problem of large websites, I learned more about Linux in depth. My colleagues have taken a higher perspective to explore the architecture.

Stage 3: hadoop Learning
1. hadoop Distributed File System: HDFS
Detailed anatomy of HDFS, understanding its working principles, and laying a solid foundation for learning Big Data
2. hadoop distributed computing framework: mapreduce
Mapreduce can be said to be a computing framework used by any big data company. It should also be mastered by every big data engineer.
3. hadoop offline system: hive
Hive is a hadoop framework with full-effort SQL computing. It is often used in work and is also the focus of face-to-face computing.
4. hadoop offline Computing System: hbase
The importance of hbase is self-evident. Even Big Data engineers who have been working for many years need to focus on hbase performance optimization.

Stage 4: zookeeper Development
Zookeeper is becoming more and more prominent in Distributed clusters, providing great convenience for the development of distributed applications. When learning zookeeper, we mainly learn the depth of zookeeper, client development, daily O & M, Web interface monitoring, and so on. Learning this part of content is also crucial for later technological learning.

Stage 5: elasticsearch distributed search

Stage 6: CDH cluster management

Stage 7: Storm Real-time Data Processing
This phase covers the internal mechanisms and principles of storm, and covers everything from data collection to Real-Time extremes to data storage and display at the front end.

Stage 8: redis cache Database
Make a full learning of redis, including its features, hash set types, string types, and so on. Finally, make a detailed learning.

Stage 9: core part of spark
This phase covers the overview of the spark ecosystem and its programming model, in-depth kernel research, spark on yarn, spark streaming stream computing principles and practices, spark SQL, multi-language programming of spark and the principle and running of sparkr.
After learning the above knowledge points, the cloud computing machine learning part is also crucial. In this part of cloud computing, we usually understand and learn about docker, virtualization KVM, and cloud platform openstack to prevent future work.

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Want to get into big data? You must plan the learning route