Recently, many people ask xiaobian how they learn big data so much. Many beginners in the initiation of the direction of big data development ideas, inevitably have some questions, how to get started? What skills should you learn? What is the learning route? Today xiaobian specially organized a big data learning route from entry to mastery. It comes with learning materials and videos. I hope I can help you.

Stage 1: Linux theory

(1) Linux foundation; (2) Linux-shell programming; (3) High concurrency: LVS load balancing; (4) High availability & Reverse proxy

Stage 2: Hadoop theory

(1) Hadoop-HDFS theory; (2) Hadoop-HDFS cluster construction; (3) HDFS 2.x & API; (4) Hadoop-MR theory;

(5) Hadoop-MR development and analysis; (6) Hadoop-MR source code analysis; (7) Hadoop-MR development case

Stage 3: Hive theory

(1) Hive introduction and installation; (2) Hive combat

Stage 4: HBase group Click the link to join the group chat [Big data exchange learning Group 2] : jq.qq.com/?_wv=1027&k…

(1) HBase introduction and installation; (2) HBase tuning

Stage 5: Redis theory

(1) Redis type; (2) Redis advanced

Stage 6: Zookeeper theory

(1) Introduction to Zookeeper; (2) Zookeeper usage

Stage 7: Scala syntax

(1) Introduction to Scala syntax; (2) Scala grammar practice

Stage 8: Spark Theory

(1) Introduction to Spark; (2) Spark code development process; (3) Spark cluster construction; (4) Spark resource scheduling principle;

(5) Spark task scheduling; (6) Spark case; (7) The two most important shuffle types of Spark;

(8) Setting up Spark HA cluster; (9) SparkSQL introduction; (10) SparkSQL combat;

(11) SparkStreaming; (12) SparkStreaming combat

Stage 9: Introduction to Machine Learning

(1) Detailed linear regression; (2) Logistic regression classification algorithm; (3) Kmeans clustering algorithm; (4) KNN classification algorithm; (5) Decision tree random forest algorithm

2018 latest Big data learning route from entry to master from zero to project actual combat, real-time transaction monitoring system, recommendation system theory, database construction and so on. Partners who need the following big data learning materials can add a group to get free, and learn big data together with industry giants.

Step 10: Elasticsearch Theory

(1) Elasticsearch search principle; (2) Elasticsearch combat

Stage 11: Storm Theory

(1) Storm introduction and code practice; (2) Storm pseudo-distributed construction and task deployment; (3) Detailed explanation of Storm architecture and DRCP principle;

(4) Virtualization theory KVM virtualization; (5) the docker

1,_ recommendation system theory and practice project Part2

2. Recommended System Theory and Practice Project, Part1

3. Real-time Transaction Monitoring System Project (Part 2)

4. Real-time Transaction Monitoring System Project (PART I)

5. User behavior Analysis System Project 1

6. User behavior Analysis System Project 2

HIVE solution for big data batch processing

8,ES open lesson part1

9,spark_streaming_

10. Detailed explanation of data warehouse construction

11, Big Data Task Scheduling

12. Kafka, a streaming data integration artifact

13, Spark public class

14. Massive log collection tool: Flume

15, Impala profile

16, Hive

17, introduction of graphs

18 Massive data High-speed access HBase database

19. Talk about Hadoop manager YARN principle

20,, distributed full text search engine ElasticSearch Part2

Conclusion: The above is the big data from the entry to the master of the learning route, and there are many projects for everyone to practice. I wish you all success in your work and a promotion!