Big data development comprehensive practical training system

Abstract

The invention relates to the technical field of big data, and discloses a big data development comprehensive practical training system which comprises a user and practical training scheme management subsystem, a basic capability practical training subsystem, a big data environment deployment practical training subsystem, a data storage practical training subsystem, a data processing practical training subsystem and a data visualization practical training subsystem. And each training subsystem realizes comprehensive skill improvement of the learner through flexible role authority management and training scheme configuration. According to the invention, practical training subsystems of basic capability, environment deployment, data storage, data processing, data visualization and the like are established, so that students are helped to promote full-link technology application from big data platform establishment to data analysis visualization. Meanwhile, the user and practical training scheme management subsystem in the system performs flexible management according to different permissions of roles such as teachers, students and administrators, supports import and modification of various types of practical training schemes, and can update the practical training content at any time according to technical progress.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a comprehensive training system for big data development. Background Art

[0002] With the rapid development of big data technology, more and more industries are beginning to adopt big data technology to drive decision-making, optimize processes and enhance competitiveness. In order to meet this demand, the market demand for big data development engineers has risen sharply. However, most of the existing big data training systems have problems such as fragmented content, lack of system, and inability to meet the needs of comprehensive training, and cannot fully cultivate students' systematic skills from basic capabilities to application development. Although some special learning materials have been launched on the market, most of them can only solve the needs in a single scenario and cannot meet the actual skill requirements in complex application scenarios. Therefore, it is particularly necessary to develop a comprehensive and systematic big data development comprehensive training system.

[0003] To this end, we propose a comprehensive training system for big data development. Summary of the invention

[0004] The present invention mainly solves the technical problems existing in the above-mentioned prior art and provides a comprehensive training system for big data development.

[0005] In order to achieve the above-mentioned objectives, the present invention adopts the following technical solutions: a comprehensive training system for big data development, including a user and training plan management subsystem, a basic ability training subsystem, a big data environment deployment training subsystem, a data storage training subsystem, a data processing training subsystem, and a data visualization training subsystem. Each training subsystem achieves comprehensive skill improvement for learners through flexible role authority management and training plan configuration.

[0006] As a further limitation of the above scheme, the user and training scheme management subsystem is used to assign user roles and supports the addition, deletion, modification and query of permissions of different roles such as administrators, teachers and learners; teachers can import, edit and delete training schemes and adjust and optimize the content as needed.

[0007] As a further limitation of the above scheme, the basic ability training subsystem includes modules such as virtual machine operation, operating system installation and configuration, programming training, and database operation training, which are used to improve learners' basic development capabilities.

[0008] As a further limitation of the above scheme, the big data environment deployment training subsystem includes modules such as distributed environment installation and deployment, data acquisition component installation and deployment, resource scheduling component installation and deployment, and data warehouse component installation and deployment, which are used to enhance learners' ability to build and configure big data platforms.

[0009] As a further limitation of the above scheme, the data storage training subsystem includes modules such as direct-attached storage, network-attached storage, storage area network, and distributed storage, which are used to deepen learners' understanding of data storage technology.

[0010] As a further limitation of the above scheme, the data processing training subsystem includes modules such as data acquisition, offline data processing, real-time data processing, and data mining, which are used to improve learners' data processing capabilities.

[0011] As a further limitation of the above scheme, the data visualization training subsystem includes mainstream visualization framework development and common component applications to enhance learners' data visualization capabilities.

[0012] The present invention provides a comprehensive training system for big data development. It has the following beneficial effects:

[0013] 1. This is a comprehensive training system for big data development. This system has designed a full range of training modules from basic capabilities to application development, effectively meeting the multi-level learning needs of big data development technology.

[0014] 2. This is a comprehensive training system for big data development. The system design is closely integrated with the latest developments in big data technology and industry needs, helping learners improve their practical operation capabilities and shorten the gap with corporate needs.

[0015] 3. This is a comprehensive training system for big data development. Through flexible user authority management and training plan adjustment, teachers can provide personalized guidance based on learners' learning situation and improve learning efficiency and quality.

[0016] 4. This is a comprehensive training system for big data development. Through systematic practical training, learners can fully master the skills required for big data development and significantly improve their employment competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented, so they have no substantial technical significance.

[0018] Figure 1 This is a system block diagram of the comprehensive training system for big data development of the present invention. DETAILED DESCRIPTION

[0019] Example 1

[0020] A comprehensive training system for big data development, such as Figure 1As shown, it includes user and training plan management subsystem, basic ability training subsystem, big data environment deployment training subsystem, data storage training subsystem, data processing training subsystem, and data visualization training subsystem. Each training subsystem achieves comprehensive skill improvement of learners through flexible role authority management and training plan configuration.

[0021] The user and training plan management subsystem is used to assign user roles and supports the addition, deletion, modification and query of permissions for different roles such as administrators, teachers and learners; teachers can import, edit and delete training plans and adjust and optimize the content as needed.

[0022] The basic skills training subsystem includes modules such as virtual machine operation, operating system installation and configuration, programming training, and database operation training, which are used to improve learners' basic development capabilities.

[0023] The big data environment deployment training subsystem includes modules such as distributed environment installation and deployment, data acquisition component installation and deployment, resource scheduling component installation and deployment, and data warehouse component installation and deployment, which are used to enhance learners' ability to build and configure big data platforms.

[0024] The data storage training subsystem includes modules such as direct-attached storage, network-attached storage, storage area network, and distributed storage, which are used to deepen learners' understanding of data storage technology.

[0025] The data processing training subsystem includes modules such as data acquisition, offline data processing, real-time data processing, and data mining, which are used to improve learners' data processing capabilities.

[0026] The data visualization training subsystem includes the development of mainstream visualization frameworks and the application of common components to enhance learners' data visualization capabilities.

[0027] The implementation method of the present invention comprises the following steps:

[0028] S1: Deploy physical equipment: including servers, network equipment and access terminals to ensure the hardware environment required for system operation;

[0029] S2: Install the training system and development components: Install the big data development comprehensive training system on the physical layer resources and deploy the required development components to ensure that the system can support different types of training tasks;

[0030] S3: Assign user roles and permissions: The administrator assigns access accounts and keys to teachers and learners to ensure that users with different roles can access corresponding functions and permissions;

[0031] S4: Introducing and optimizing practical training plans: Teachers introduce corresponding practical training plans based on students’ learning progress and needs, and adjust and optimize them according to the learning situation to achieve personalized teaching;

[0032] S5: Release the training plan: The teacher releases the optimized training plan and assigns tasks to learners based on the course schedule and student abilities.

[0033] S6: Completion and submission of practical training tasks: learners complete various tasks according to the requirements of the practical training plan and submit the practical training results within the specified time;

[0034] S7: Practical training review and scoring: The teacher reviews the practical training results submitted by the learners and gives scores to evaluate the learners' learning effects.

[0035] Example 2

[0036] Based on Example 1, this example is about system deployment and hardware environment configuration. System deployment first requires configuring the required hardware devices at the physical level. This step includes the following operations:

[0037] S1: Deploy physical equipment. The physical equipment of the system includes servers, network equipment, and access terminals. The server is used to carry the core applications of the big data development comprehensive training system, including storage and computing resources. The network equipment is used to support communication and data transmission between modules in the system. The access terminal includes PC terminals or mobile terminals used by teachers and learners to access the system and perform tasks.

[0038] The server configuration should take into account the computing and storage requirements of big data processing. It is recommended to use high-performance computing servers (such as high-frequency CPU, large memory, high-speed storage) and distributed storage architecture to ensure the stability and response speed of the system during large-scale data processing and multi-user concurrent operations.

[0039] S2: Install development components. On physical devices, administrators need to install relevant development components and platforms that support big data development, such as Hadoop, Spark, Flink, Hive, HBase, etc. Specifically, administrators need to install these components through scripts provided by the platform or manually, and configure them. These components support big data processing, storage, analysis and other functions, and are the basis for implementing various practical training tasks.

[0040] S3: Configure a virtualized environment. To ensure that each learner can operate in an independent environment, the system should support virtual machine (VM) technology. The administrator needs to allocate a virtual machine to each learner and install the operating system, necessary development tools, and big data components in the virtual machine. The configuration of the virtualized environment ensures that learners can conduct experiments in an isolated environment, avoiding operational interference between different learners, and is also easy to manage and monitor.

[0041] S4: Network and security configuration. Network devices need to be configured with appropriate bandwidth and firewall policies to ensure smooth data transmission and prevent external attacks. All communications and data transmissions need to be encrypted to ensure data security. Administrators should ensure that teachers and learners can access the system securely through VPN or other secure channels, especially in remote education scenarios.

[0042] Example 3

[0043] Based on Example 1, this example focuses on user and training program management. A key feature of this system is the management of different user roles, ensuring that teachers, learners, and administrators can access different functions of the system according to their respective permissions. This example will demonstrate how to customize training programs according to the different needs of learners and optimize the teaching process through role permission management.

[0044] User role management: In the system management module, the administrator is responsible for assigning different roles and permissions to teachers and learners. The specific steps are as follows:

[0045] The administrator creates a user account, enters basic user information (such as name, student number, role, etc.), and assigns access rights to it.

[0046] The teacher role is given the authority to import, edit, and delete training plans, and can flexibly adjust the training content according to teaching needs.

[0047] The learner role can only view the assigned training tasks and submit training results to ensure that the system's operating permissions are not abused.

[0048] Training plan management and optimization: Teachers can import training plans developed with big data through the user and training plan management subsystem. The specific steps are as follows:

[0049] Based on the course content and the actual level of learners, teachers introduce practical training plans for modules such as basic capabilities, big data environment deployment, data storage, data processing and data visualization.

[0050] The training plan includes detailed task instructions, time requirements, reference materials and evaluation criteria. Teachers can adjust these plans according to actual needs, such as updating the difficulty of tasks or modifying the content of tasks to meet the needs of different learners.

[0051] When learners submit their training results, teachers can review and grade them through the system. Teachers can give targeted feedback to help learners improve.

[0052] Dynamic training program update: During the operation of the system, big data technology will be continuously updated and developed. Therefore, this system needs to support dynamic updates of training programs. When a big data technology component or development tool version is updated in the system, teachers can quickly adjust and update the corresponding training tasks and programs. Administrators can upgrade component versions in a timely manner to ensure that the training content is connected with the actual technology.

[0053] Example 4

[0054] Based on Example 1, this example is about the use of the big data environment deployment training subsystem. The big data environment deployment is a crucial step in the big data development process, involving the construction and configuration of the big data platform. This example shows how to deploy the training subsystem through the big data environment to help learners master the configuration and management of the big data platform.

[0055] Distributed environment installation and configuration:

[0056] This training subsystem includes a distributed installation and deployment module for the big data environment. The goal is to enable learners to master the infrastructure and deployment technology of the big data platform. The administrator provides learners with a set of distributed installation training tasks, including:

[0057] Install and configure the Hadoop cluster. Learners need to set the cluster's node information, HDFS (Hadoop Distributed File System) and YARN (resource manager) through configuration files to ensure that the cluster can efficiently process large-scale data.

[0058] Configure and deploy a Spark cluster. Learners configure Spark's cluster mode and resource scheduling to enable it to run on a cluster and process big data tasks.

[0059] Configure database components such as HBase and Hive. Learners need to install and configure these database components in the cluster to ensure that they can be seamlessly integrated with the Hadoop ecosystem.

[0060] Installation and configuration of data acquisition and processing components:

[0061] In addition to the basic big data platform, learners also need to configure data collection and processing components. This task aims to enable learners to master the data flow and processing process from the data source to the big data platform. Specific practical training content includes:

[0062] Install and configure data collection tools, such as Flume and Kafka, to help learners understand how to collect data from various data sources (such as logs, sensors, etc.) and transmit them to the data platform.

[0063] To configure resource scheduling tools such as Apache Mesos and YARN, learners need to set scheduling policies based on actual resource requirements to ensure that tasks can be efficiently allocated to each node in the cluster.

[0064] Install and configure data warehouse components, such as Hive, Impala, etc. Learners need to understand how to use the SQL query language to analyze large-scale data.

[0065] Debugging and troubleshooting:

[0066] During the environment deployment process, learners may encounter various problems, such as component configuration errors, resource conflicts, performance bottlenecks, etc. The system provides a special debugging and troubleshooting module to help learners solve practical problems through log analysis, performance monitoring and troubleshooting. This not only improves learners' technical capabilities, but also allows them to have a deeper understanding of the stability and reliability of the big data environment.

[0067] System monitoring and operation and maintenance management:

[0068] After the deployment is completed, learners also need to understand the operating status of the big data platform through the system monitoring and operation and maintenance module. This module includes:

[0069] Use tools such as Apache Ambari or Zabbix to monitor the system and get real-time information about the status of each node, cluster load, and data processing performance.

[0070] Configure an alarm mechanism to ensure that failures can be discovered and handled in a timely manner to avoid system crashes.

[0071] Learners also need to master the resource scheduling, fault-tolerance mechanisms and data backup strategies of the big data platform to ensure the high availability of the platform in the face of emergencies.

[0072] Example 5

[0073] Based on Example 1, this example is about the use of the data processing and visualization training subsystem. Data processing and visualization are key links in big data development. The acquisition, storage, cleaning, conversion and display of data directly affect the value of data. This example shows how to improve learners' data processing and visualization capabilities through the data storage, data processing and data visualization training subsystems.

[0074] 1. Data storage training subsystem

[0075] The data storage module helps learners master the design and implementation of data storage solutions by simulating application scenarios of different storage technologies. Specific tasks include:

[0076] Distributed storage configuration and management: Learners understand how data is stored in a distributed environment by configuring and using storage technologies such as HDFS (Hadoop Distributed File System) and HBase. Tasks include:

[0077] Store large data sets in HDFS and use MapReduce for data processing.

[0078] Configure HBase table structure and indexes to achieve efficient random read operations.

[0079] By simulating fault recovery and data redundancy strategies, learners can understand data high availability and fault tolerance mechanisms.

[0080] Database technology usage: The system supports different types of database storage, such as relational databases (MySQL, PostgreSQL, etc.) and NoSQL databases (MongoDB, Cassandra, etc.). Learners need to complete tasks such as:

[0081] Use SQL statements to query, insert, update, and delete data.

[0082] Storing and querying unstructured data in NoSQL databases helps learners understand the advantages and limitations of different databases in data processing and querying.

[0083] Data storage optimization: In this module, learners also need to optimize data storage to ensure the efficiency of the data storage solution. For example:

[0084] Configure the HBase compression algorithm to improve storage efficiency.

[0085] Adjust the block size of HDFS to optimize the performance of the file system.

[0086] 2. Data processing training subsystem

[0087] In the data processing module, learners need to complete offline data processing and real-time data processing tasks, including data cleaning, conversion, analysis, etc. Specific operations include:

[0088] Offline data processing and ETL tasks: Learners use Apache Spark to perform batch processing tasks, covering data cleaning, filtering, transformation, and aggregation operations. Tasks include:

[0089] Write Spark programs to process raw data sets, clean dirty data and perform format conversion.

[0090] Use Spark SQL to perform complex data queries and aggregation operations to help learners understand how to optimize query performance.

[0091] Use Spark MLlib to perform simple machine learning tasks such as classification and regression analysis.

[0092] Streaming data processing: To help learners master the technology of real-time data processing, the system provides integrated applications of Flink and Kafka. The tasks that learners need to complete include:

[0093] To use Flink to process real-time data streams, learners need to design the processing logic of real-time streaming data, such as filtering, transformation, aggregation, etc.

[0094] Configure Kafka as the data transmission pipeline and use Flink to read the data in Kafka in real time for processing.

[0095] Implement real-time data monitoring and alarm mechanisms to help learners understand the key issues of latency, throughput, and fault tolerance in streaming data processing.

[0096] Performance tuning and troubleshooting: Performance optimization is crucial when processing large-scale data. Learners need to learn how to tune the performance of platforms such as Spark and Flink and solve common performance bottlenecks such as task delays and uneven resource utilization. Specific tasks include:

[0097] Analyze and optimize the execution plan of Spark jobs to reduce unnecessary data transmission.

[0098] Optimize the window strategy of Flink jobs to improve the throughput and latency of real-time processing.

[0099] Use debugging tools to find bottlenecks in your job and optimize its execution efficiency.

[0100] 3. Data visualization training subsystem

[0101] Data visualization is an important part of big data analysis, which can help learners transform complex data information into easy-to-understand charts and graphs. This training subsystem helps learners master data presentation skills by providing various data visualization tools and tasks.

[0102] Data Visualization with Tableau and Power BI: Learners need to use popular visualization tools such as Tableau and Power BI to generate data analysis reports. Specific tasks include:

[0103] Import datasets and use these tools to create charts, maps, dashboards, and more.

[0104] Design interactive visualization interfaces to help learners present complex data into easy-to-analyze views.

[0105] Configure automatic refresh and real-time data visualization to help learners achieve dynamic data presentation.

[0106] Customized visualization using Python: Learners can achieve more flexible customized data visualization through libraries such as Matplotlib, Seaborn, and Plotly in Python. Tasks include:

[0107] Use Matplotlib and Seaborn to draw common statistical charts such as bar charts, line charts, heat maps, etc.

[0108] Use Plotly to create interactive charts and graphs to help learners integrate data visualization into web applications.

[0109] Customize the style and interactive behavior of the chart by writing code to meet different data display needs.

[0110] Real-time data visualization and monitoring: In a real-time data processing system, learners also need to understand how to display real-time data on a dynamic dashboard. Specific operations include:

[0111] Use tools like Dash or Bokeh to visualize real-time data.

[0112] Integrate Kafka or Flink's real-time data stream to update data views in real time.

[0113] Configure charts and dashboards with dynamic data updates to help learners monitor the results of big data processing in real time.

[0114] 4. Project practice and comprehensive evaluation

[0115] After studying the above modules, learners will complete a comprehensive project to consolidate the knowledge and skills they have learned. The project requires learners to independently complete the entire process from data collection, storage, processing to visualization, simulating the big data development workflow in real scenarios. Specific content includes:

[0116] Data collection and storage design: Learners need to choose the appropriate technology stack (such as Kafka, HDFS, HBase, etc.) to complete the data collection and storage design required for the project.

[0117] Data processing and analysis: According to project requirements, learners need to use Spark or Flink for data cleaning, transformation and analysis.

[0118] Data visualization and reporting: Present the analysis results through Tableau, Power BI or Python visualization tools, and write a complete analysis report.

[0119] Project presentation and defense: After the project is completed, learners need to present their results through the system and accept evaluation and defense from teachers.

[0120] Through the above several embodiments, the comprehensive training system for big data development of the present invention can provide learners with comprehensive practical experience, covering the entire development process from environment deployment to data processing and visualization. Each embodiment helps learners master the core skills of big data development through practical technical operations and improve their application capabilities in real environments.

[0121] This system can not only help learners master a single technology, but also cultivate their ability to use multiple technologies in a comprehensive manner for data processing, storage, analysis and visualization. Through systematic training content, learners can better deal with practical problems in the industry and improve their competitiveness in the field of big data.

[0122] Working principle of the present invention:

[0123] The working principle of this system is based on modular training design, and all training tasks can be flexibly configured according to actual needs. Each subsystem provides a complete set of operation and application scenarios according to its goals and functions:

[0124] The basic skills training subsystem helps learners master the basic skills of big data development and form solid programming and database operation capabilities through virtual machine environment and system configuration modules.

[0125] The big data environment deployment training subsystem focuses on the installation, configuration, and debugging of the big data platform and related components, helping learners understand the architecture of the big data system and the collaboration between components.

[0126] The data storage training subsystem helps learners understand the advantages and disadvantages of different data storage methods and master the engineering practice of data storage through the practice of storage technology.

[0127] The data processing training subsystem focuses on data collection, processing and analysis, covering modules such as offline and real-time processing, data mining, etc., to enhance learners' data processing and mining capabilities.

[0128] The data visualization training subsystem uses common visualization tools and techniques to help learners improve their data visualization and front-end development capabilities, and form an effective display of big data analysis results.

[0129] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A comprehensive training system for big data development, characterized in that: It includes user and training plan management subsystem, basic ability training subsystem, big data environment deployment training subsystem, data storage training subsystem, data processing training subsystem, and data visualization training subsystem. Each training subsystem achieves comprehensive skill improvement of learners through flexible role authority management and training plan configuration.

2. A comprehensive training system for big data development according to claim 1, characterized in that: The user and training program management subsystem is used to assign user roles and supports the addition, deletion, modification and query of permissions of different roles such as administrators, teachers, and learners; teachers can import, edit, and delete training programs, and adjust and optimize content as needed.

3. A comprehensive training system for big data development according to claim 1, characterized in that: The basic ability training subsystem includes modules such as virtual machine operation, operating system installation and configuration, programming training, and database operation training, which are used to improve learners' basic development capabilities.

4. The big data development comprehensive training system according to claim 1, characterized in that: The big data environment deployment training subsystem includes modules such as distributed environment installation and deployment, data acquisition component installation and deployment, resource scheduling component installation and deployment, and data warehouse component installation and deployment, which are used to improve learners' ability to build and configure big data platforms.

5. The big data development comprehensive training system according to claim 1, characterized in that: The data storage training subsystem includes modules such as direct-attached storage, network-attached storage, storage area network, and distributed storage, which are used to deepen learners' understanding of data storage technology.

6. A comprehensive training system for big data development according to claim 1, characterized in that: The data processing training subsystem includes modules such as data acquisition, offline data processing, real-time data processing, and data mining, which are used to improve learners' data processing capabilities.

7. The big data development comprehensive training system according to claim 1, characterized in that: The data visualization training subsystem includes mainstream visualization framework development and common component applications, which are used to improve learners' data visualization capabilities.

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