20,170 Basic Big Data Employment course (full network, 856 hours)
Course View Address: http://www.xuetuwuyou.com/course/181
The course out of self-study, worry-free network: http://www.xuetuwuyou.com
This course is the most complete set of big data employment courses built by the team of wind-dancing smoke teachers in four months. Can be said to be completely 0 programming basis to start, one to the employment! The course is divided into 2 modules, 14 courses, 65 chapters and a total of 856 hours!
Course Outline:
One, Java module course
Lesson one, "Big data must Know"-Java negative basic literacy
01. Java base syntax, variables, data types, and operators
02. Select Structure
03. Cyclic structure
04. Advanced Cycle Structure
05. Arrays
Lesson two, "Big data Must Know"-Java OOP object-oriented programming
06. Classes and objects, methods
07, the Project Human Machine scissors
08. Project DVD Management System
09. Inheritance, encapsulation, polymorphism
10, multi-state, interface
11, the project actual combat: Audition Dance Group
12, exception, date class, collection, generic collection
13. Java IO Stream Technology & XML operations
Lesson three, "Big data must know"-MySQL database development
14. mysql database--initial MySQL
15. mysql Database--sql advanced
16, MySQL database-multi-table query and stored procedures
Lesson four, "Big data must Know"-Java core programming
17. Using JDBC to manipulate database in Java
18. Project case: Realizing book management system based on Jdbc+mysql
19. The basis of multithreaded programming
20. Multithreading Advanced Programming
21. Network Programming
Lesson five, "Big data must be known"-Java Advanced Features
22. Java Advanced Features-reflection, serialization
23. Project Case (5): Instant chat system
24. Git version control system, MAVEN project management build tool
25. Project combat: Small commodity invoicing management system
Second, Big Data module course
Course Vi. "Fundamentals of Big Data Development"-LINUX courses
26. Environment and Basic operation
27, the use of common Linux commands
28. Linux Software Installation and Shell programming
Lesson seven, "Big Data distributed infrastructure"-Hadoop explained
29. Introduction to Hadoop and pseudo-distributed environment
30. MapReduce Programming and Operation Process
31. Website case analysis and Hadoop distributed cluster environment
32. Mapreduceshuffle and Zookeeper Frame
33. HDFS ha and two-time sequencing
34. YARN Resource Management and MapReduce Join
Lesson eight, "Big Data Warehouse"-HIVE details
35. Hive Basic architecture and environment deployment
36. Hive Common DML, UDF and connection mode
37, Sqoop Sqoop and user behavior analysis case
38. Hive Complex user behavior case analysis and optimization
39. Zeus Task Resource Scheduling tool
Lesson nine, "Big data distributed open source data"-hbase from getting started to mastering
40. Hbase basic use and storage design
41. Hbase enterprise application and integration with MapReduce
42, Case: hbase Design and Enterprise optimization
Lesson ten, "Big Data Visualization Technology"-Echarts detailed
43. Big Data Visualization Technology: Overview of Visualization technology and Echarts Introduction
44, Big Data visualization technology: Echarts Chart detailed
45, Big Data visualization technology: Echarts Advanced and comprehensive case of practical combat
Course Xi. "HADOOP project"
46. Big Data offline Project: Enterprise Big Data project business and design
47. Big Data Offline Project: Data acquisition Framework Flume
48, Big Data offline Project: Nginx+flume Realization Data acquisition
49, Big Data offline Project: ETL Business Analysis and implementation (i)
50, Big Data offline Project: ETL Business Analysis and Implementation (II)
51, Big Data offline project: The idea of data analysis and code implementation
52, Big Data offline Project: Hourly analysis and visual display
Lesson 12, "spark from the start to getting Started"-Spark course
53. SPARK Data Analysis: Scala programming (i)
54. SPARK Data Analysis: Scala programming (II)
55. Spark data analysis: Spark Core (i)
56. Spark data analysis: Spark Core (ii)
57. Spark Data analysis: Spark SQL
58. Spark data analysis: Spark streaming and Kafka
59. Spark data analysis: Spark programming based on Java and Python
Lesson 13, "real-time streaming computing framework"-Storm technology details
60. Storm: Architecture, environment and use
61, Storm:trident use
62, Storm: E-commerce real-time sales statistics
Course 14, "Big Data Enterprise Platform Application"
63. High Salary interview: Enterprise Big Data Platform (i)
64, high-paying interview: Enterprise Big Data Platform (ii)
65. High-paying interview: Big Data interview
For detailed catalogue, please enter: http://www.xuetuwuyou.com/course/181/tasks
Ii. objectives of the course:
1, understand the history of Hadoop and the current development of the current situation, as well as the technical characteristics of Hadoop, so as to grasp the distributed computing framework and the future development direction, in the big Data era for the enterprise's technology selection and architecture design to provide decision-making reference.
2. Let the students have a clear and comprehensive understanding of big data technology commonly used in the whole ecosystem of big data, can plan and deploy the Hadoop cluster of production environment independently, master the basic operation and maintenance ideas and methods of Hadoop, manage and optimize the Hadoop cluster. Ability to use big data technology to store, manage, analyze, and ultimately create value for the enterprise's big data
Third, the direction of employment:
As this course covers a wide range of technical aspects, there are many employment directions, including but not limited to the following major jobs:
1. Hadoop Big Data Development Engineer
2. Hive Big Data Development Engineer
3. Storm Big Data Development Engineer
4. Spark Big Data Development Engineer
5. Big Data Analyst
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