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Combining Years Of Experience In The Education Industry, We Have Independently Developed A Big Data Experimental Teaching System, Which Focuses On Big Data Core Technology Experimental Teaching. It Aims To Provide Colleges And Universities With An Experimental Training Environment For Big Data Talent Training, Improve Students’ Practical Skills, And Combine It With Colleges And Universities’ Big Data Theoretical Teaching To Cultivate Practical Talents With A Solid Theoretical Foundation And Enhance Students’ Comprehensive Market Competitiveness.
The Big Data Experimental Teaching System Is Based On The Cloud Platform Architecture And Provides Users With A Big Data Experimental Environment, Which Is Very Suitable For The Experimental Teaching Needs Of Colleges And Universities. The Experimental Content Covers Professional Basic Content Such As Hadoop, Hive, Hdfs, Spark, Etc., And Also Includes Data Collection And Log Analysis Applications, Data Migration, Data Statistical Analysis, Data Mining And Data Visualization And Other Data Processing Application Related Technical Experimental Operations, Providing Students With The Entire Core Technology Experimental Teaching Process From Big Data Environment Construction To Data Processing, Which Can Support The Development Of Experimental Content Of Courses At All Stages Of Big Data Professional Talent Training.
The Big Data Experimental Teaching System Is A Product That Fits The Characteristics Of Big Data Majors In Universities. It Integrates Years Of Industry Accumulation And The Industry’s Most Cutting-edge Big Data Platform Technology To Provide Simple And Easy-to-use Operation And Management Functions, And Tailors The Experimental Development And Debugging Environment, Experimental Operation Management, And Teaching Management Functions For Teaching And Training Scenarios. At The Same Time, It Provides Rich Big Data Experimental Training Resources Including Professional Core Courses, Project Training Courses, Big Data Development Courses, Etc. It Is A Comprehensive System That Combines Teaching, Experiments, And Training To Focus On Big Data Application Technology And Practical Skills Training.
The Product Design Of The Big Data Experimental Teaching System Is Divided Into Five Levels From Bottom To Top, Namely: Hardware Cluster Basic Platform, Virtualization Support Cloud Platform, Business Application Management Platform, Teaching And Training Supporting Resources, And User Interface.
The Big Data Experimental Teaching System Consists Of Six Modules: System Management Module, Course Teaching Module, Practical Training Module, Score Management Module, Examination Management Module, And Learning Experience Module. It Includes More Than 40 Big Data Courses, More Than 900 Experiments, And More Than Ten Big Data Comprehensive Industry Application Cases. The Experimental Content Design Is Centered On The Construction Of The Big Data Platform To The Visualization Of Big Data Mining, Covering The Core Technical Content Teaching Of Each Stage Of Big Data Analysis And Processing.
• It Adopts A Distributed Storage Architecture And Provides A Multi-copy Data Protection Mechanism To Ensure The High Reliability Of The Customer’s Private Cloud Management Platform Data.
• The Solution Integrates The Local Large-capacity Hard Disks Of The Hyper-converged Computing Nodes To Build A Common Shared Storage Resource Pool For Storing General Business Data And Virtual Machine Files.
• Build A High-speed Shared Storage Resource Pool Through The Local Ssd Hard Disks Of The Hyper-converged Computing Nodes To Store Database Files.
Provide Support For The Preparation Of Professional Course Plans, Practical Training Syllabuses And Other Curriculum Plans; Cooperate With Enterprises To Jointly Develop Teaching Resources And Special Teaching Materials, And Jointly Publish Teaching Materials; Provide Students With Internship Opportunities In Enterprises.
The Content Design Is In Line With The Teaching Needs Of Universities. The System Includes More Than 40 Big Data Courses And More Than 30 Big Data Comprehensive Training Courses. The Course Content Is Designed To Be More In Line With The Learning Scenarios Of University Students.
To Meet The Needs Of Multi-scenario Architecture, Simplebdt Adopts Docker And Kvm Hybrid Virtualization Cloud Platform Technology, Which Can Support Experimental Images Such As Information Security, Cloud Computing, Big Data, Artificial Intelligence, Etc. In Different Scenarios, Realizing "One Platform, Multiple Uses".
It Includes Four Experimental Modes: Jupyter, Vnc, Webssh, And Webide. It Also Supports Single-player Mode, Team Mode, Continuous Experiment Mode, Fully Open/fully Closed Answer Mode, And Semi-closed Monitoring Mode For The Rate Of Non-mastery.
By Visually Displaying Teacher Teaching Information And Student Learning Information, Teachers Can Adjust Teaching Plans According To Students' Learning Situations And Make More Targeted Teaching Arrangements.
Teachers Can Make Reservations For The Resources Needed For Classes, And The System Will Reserve Class Resources For Students In Advance; Teachers Can Manually Release The Online Environment For Free Learning Students, Saving Resources And Improving The Fluency Of Teaching Practice.
