CN110874356A - Cloud big data system and construction method thereof - Google Patents
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Abstract
The invention relates to the technical field of data processing, and discloses a cloud big data system and a construction method thereof, which solve the technical problems of multi-type data source access, data online analysis and visualization of the cloud big data system.
Description
Technical Field
The disclosure relates to the technical field of data processing, in particular to a cloud big data system and a construction method thereof.
Background
With the interactive fusion of information technology and human production and life, global data shows the characteristics of explosive growth and mass aggregation, and has great influence on economic development, social governance and people's life. In recent years, with the rapid development of big data technology, continuous update and iteration of software and hardware, the big data technology has been developed in various industries.
Through big data analysis of the users, enterprises can master user behavior preference, accurate recommendation and marketing are realized, and the research and development of products and the upgrade of services are driven by clients, so that products and services which are guided by the users are provided, and the number of users of the enterprises is increased; through the big data analysis of the industry, an enterprise can know a competitor, know the product dynamics, adjust the strategy and optimize the product, so that the enterprise is more active in a fierce environment; through big data analysis of the business, an enterprise can better master the current development situation and optimize resource allocation, so that the limited resources can generate greater benefits.
With the deployment of 5G, the requirements for big data will be higher by everything interconnection. However, the existing big data system has some defects:
1. the system is built with time and labor consumption, complex operation and maintenance and high cost: the big data related technology is very wide, and the used software is very much, so that the time and the labor are consumed for an enterprise to build a big data platform, the operation and the maintenance are very complicated in the later period, and very large cost is brought to the enterprise;
2. the requirements on the professional level of technicians and analysts are high: the big data analysis technology is complex, so professional big data personnel and data analysis personnel are needed, and the difference of different industries is large, so experts in the field are needed, and the requirement on the technical level of the personnel is high;
3. the existing data is difficult to integrate: enterprise data sources are very wide, text data, data in relational and non-relational databases, protocol data generated by internet of things equipment, data in message queues, network marketing data and the like, different modes of connecting different data to a large data platform are different, and enterprise access is very complex;
4. the development cycle is slow: both data analysis and data visualization require professional technology and service capability, and in addition, the variability of service scenes, the traditional method has a long development period and needs continuous upgrading and iteration;
5. the quality of the data is difficult to monitor: the key of data analysis is data itself, and the source of the data is influenced by many factors such as network, collecting equipment, etc., so the data may be transmitted to a terminal, some information may not be collected, and the important contents in the traditional analysis only depend on the later artificial analysis, have serious hysteresis, and have very serious influence on the subsequent analysis;
6. system security and data security management are complex: the large data system is complicated, and various problems of hardware and software failure, data plaintext storage, data loss and the like can occur in the system.
In summary, the existing cloud big data system is too complex, and has great difficulty in integrating multiple types of data sources and processing and managing data.
Disclosure of Invention
The invention provides a cloud big data system and a construction method thereof, which enable the cloud big data system to realize the technical purposes of multi-type data source integration and data online analysis and visualization.
The technical purpose of the present disclosure is achieved by the following technical solutions:
a cloud big data system, comprising:
the data access module accesses a multi-type data source D and comprises:
the first type selection unit is used for dividing the types of the accessed data sources D to obtain the parent types of the data sources D;
the second type selection unit is used for dividing the father type to obtain the subtype of the data source D;
a first setting unit that performs first setting on the data source D according to the subtype;
the verification unit is used for verifying the data source D which completes the first setting, if the verification is passed, the data source D is transferred to the preview unit, and if the verification is not passed, the data source D is transferred to the first setting unit;
the previewing unit previews the verified data source D;
a third setting unit, after the preview is finished, performing third setting on the data source D to obtain a data source
Submitting the data source
。
A data processing module for processing the data source
Carrying out data processing;
a data analysis module for processing the data source
Carrying out data analysis to obtain an analysis result;
and the data application module generates a visual page or an intelligent report according to the analysis result.
Further, the data access module further comprises a second setting unit, after the preview is completed, the second setting unit performs second setting on the data source D, and after the second setting is completed, the data source D performs third setting.
Further, the first setting unit includes a format setting block and a flow setting block; the second setting unit comprises an attribute filtering setting block, an attribute renaming setting block, an attribute format conversion setting block, an attribute expansion setting block and an attribute decryption setting block; the third setting unit sets a block for field information, and the field information includes a data source name, an import mode, and an update period.
Further, the import mode comprises full-scale import and incremental import, and the incremental import comprises timing import and real-time import.
Furthermore, the data processing mode of the data processing module comprises data cleaning, data screening, data filtering, multi-data conversion, data integration and data stipulation.
Further, the data analysis module includes:
the analysis unit is used for carrying out data statistics, data classification, data regression and data clustering on the data;
the mining unit is used for mining the data to acquire useful information;
the visual page comprises a WEB page, a mobile phone APP page and a large screen, and the large screen comprises an LED large screen and a television large screen.
Furthermore, the system also comprises a data sharing module and a safety management module, wherein the data sharing module realizes data sharing, and the safety management module carries out safety management on the cloud big data system and the data.
Further, the parent type of the data source includes text data, relational data, non-relational data, big data platform data, message queue data, internet of things device protocol data, API data, and public data.
A construction method of a cloud big data system comprises the following steps:
accessing a multi-type data source D;
dividing the types of the accessed data sources D to obtain the parent types of the data sources D;
dividing the father type to obtain a subtype of the data source D;
performing first setting on the data source D according to the subtype;
verifying the data source D which completes the first setting, previewing if the verification is passed, and returning to the first setting if the verification is not passed;
previewing the data source D passing the verification;
thirdly setting the data source D after the preview is finished to obtain a data source
Submitting the data source
。
For the data source
Carrying out data processing;
the data source after data processing
Carrying out data analysis to obtain an analysis result;
and generating a visual page or an intelligent report according to the analysis result.
And further, the access process of the data source also comprises second setting, after the preview is finished, the second setting is carried out on the data source D, and after the second setting is finished, the third setting is carried out on the data source D.
Further, the first setting comprises a format setting and a flow setting; the second setting comprises attribute filtering, attribute renaming, attribute format conversion, attribute expansion and attribute decryption; the third setting is set as field information, and the field information comprises a data source name, an importing mode and an updating period.
Further, the import mode comprises full-scale import and incremental import, and the incremental import comprises timing import and real-time import.
Further, the data processing mode comprises data cleaning, data screening, data filtering, multi-data conversion, data integration and data specification.
Further, the data analysis comprises:
carrying out data statistics, data classification, data regression and data clustering on the data;
mining data to obtain useful information;
the visual page comprises a WEB page, a mobile phone APP page and a large screen, and the large screen comprises an LED large screen and a television large screen.
Further, the parent type of the data source includes text data, relational data, non-relational data, big data platform data, message queue data, internet of things device protocol data, API data, and public data.
In conclusion, the beneficial effects of the present disclosure are: the cloud large data system is simple to use, data are simple to butt joint, data analysis can be achieved without any technical background, operation is simple, data in the visual page are calculated in real time, and instantaneity of the data is guaranteed.
Drawings
FIG. 1 is a system framework diagram of the present invention;
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The present disclosure is described in further detail below with reference to the attached drawing figures.
The present disclosure mainly addresses the complexity scenario corresponding to big data, such as: the method comprises the steps of building and operating a platform, system safety, data safety management, multi-type data source integration, data processing and analysis, data visualization, intelligent reporting, data quality monitoring and the like. The present disclosure has applications in areas including, but not limited to, transportation, medical, banking, telecommunications, manufacturing, government, retail, and e-commerce.
In the description of the present disclosure, the terms "first", "second", and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated, but merely as distinguishing between different components.
Fig. 1 is a frame diagram of the system of the present disclosure, in which a cloud big data system includes a data access module, a data processing module, a data analysis module, and a data application module, the data access module includes a first type selection unit, a second type selection unit, a first setting unit, a verification unit, a preview unit, a second setting unit, and a third setting unit, and the first type selection unit divides the type of an accessed data source D to obtain a parent type of the data source D; the second type selection unit divides the parent type to obtain the subtype of the data source D; the first setting unit performs first setting on the data source D according to the subtype; the verification unit verifies the data source D which completes the first setting, if the verification is passed, the preview unit is switched to, and if the verification is not passed, the preview unit is switched back to the first setting unit; the previewing unit previews the verified data source D; after the preview is finished, the second setting unit performs second setting on the data source D, and after the second setting is finished, the third setting unit performs third setting on the data source D to obtain the data source
Submitting data sources
. Data processing module for data source
And processing the data, performing data analysis on the processed data by using a data analysis module to obtain an analysis result, and generating a visual page or an intelligent report according to the analysis result by using a data application module.
The first setting unit comprises a format setting block and a flow setting block, wherein the format setting block sets a data format, such as a text data format, an Internet of things protocol format, link information of a relational/non-relational database, an address of a big data platform, account information and the like; the flow setting block sets the relevant flow of the original data. And after the first setting is finished, the parameter is checked by the checking unit when the parameter is submitted, and if the parameter is invalid, the parameter is returned to the first setting unit again to carry out the first setting.
The second setting unit comprises an attribute filtering setting block, an attribute renaming setting block, an attribute format conversion setting block, an attribute expansion setting block, an attribute decryption setting block and the like, and is an unnecessary unit which can determine whether to select the setting unit according to actual conditions.
The third setting unit sets blocks for field information, and the field information comprises a data source name, an import mode and an update period. For example, a data source name, an importing mode, an updating period and the like are set, wherein the importing mode includes full importing and incremental importing, the full importing refers to importing data in a data source D to the cloud big data system at one time, and the incremental importing refers to importing data in the data source D to the system in multiple times. The incremental import also comprises timing import or real-time import, wherein the timing import refers to periodic import according to set time, and the real-time import refers to real-time continuous access to data. When field information is set, field items can be redefined, original values of the fields can also be kept, and finally, the cloud big data system conducts data import and timing updating operation according to set parameters.
The data processing module is used for carrying out data cleaning, data screening, data filtering, multi-data conversion, data integration and data specification on the data source. The data analysis module comprises an analysis unit and an excavation unit, the analysis unit carries out data statistics, data classification, data regression and data clustering on data, the analysis mode is such as a click mode (namely a corresponding analysis method is directly selected), an SQL-like mode (namely a mode similar to SQL grammar is input), and a self-defined algorithm (a user submits a model algorithm), and the system carries out automatic calculation on the data according to the difference of the data source form without user interference; and the mining unit mines the data to acquire useful information. The cloud big data system further comprises a data sharing module, and large-scale sharing of data can be achieved.
FIG. 2 is a flow chart of the method of the present disclosure, accessing a multi-type data source D, and dividing the type of the accessed data source D to obtain a numberAccording to the parent type of the source D; dividing the parent type to obtain a subtype of the data source D; performing first setting on the data source D according to the subtype; verifying the data source D which completes the first setting, previewing if the verification is passed, and returning to the first setting if the verification is not passed; previewing the verified data source D; thirdly setting the data source D after the preview is finished to obtain the data source
Submitting data sources
. For data source
Carrying out data processing; for data source after data processing
Carrying out data analysis to obtain an analysis result; and generating a visual page or an intelligent report according to the analysis result.
The data processing method comprises data cleaning (such as missing value and abnormal value processing and the like), data screening, data filtering, data conversion, multiple data integration, data reduction and the like. After the data processing is finished, the data can be analyzed and mined according to specific services and standards to obtain useful information, namely the final visual display or intelligent report of the data.
The first setting, the second setting, and the third setting are described with reference to the system of the present disclosure and will not be described herein.
In the above, the types of the data source include text data, relational data, non-relational data, a big data platform database, a message queue database, internet of things device protocol data, API data, and public data. The format of the text data is such as plain text, excel, csv; the data in the relational database comprises MySQL, Oracle, Access, DB2, PostgreSQL, SQLServer, Hive, SQLite and the like; data in non-relational databases such as redis, MongoDB, neo4j, and the like; the large data platform data comprises hdfs, hbase, hive and the like; data in the message queue database such as kafka, mqtt, etc.; the internet of things device protocol data specifies the type of a decoder, such as a specified length decoder, a specified symbol decoder, a line feed symbol decoder and a complex decoder, and also can optionally provide industrial national standard decoders, such as GB/T32960, JT808 and the like, and a user can also customize the decoder; selecting an API access mode such as HTTP, HTTPS, Websocket and the like for API data; the public data is public data collected by the platform or data in a data set shared by others.
In addition, the data source access modes are different according to different data source types, and specifically, for text data, single import, data addition, data replacement and the like can be performed; the data in the database can be imported in a full-quantity or incremental mode; the data in the message queue can be imported in a real-time or timing mode.
The visual page comprises a WEB page, a mobile phone APP page and a large screen, the large screen comprises an LED large screen, a television large screen and the like, and generally speaking, the screen for displaying belongs to the visual page. The visual page and the intelligent report can be automatically updated according to the update of the data, so that various forms of display and report issuing are realized, and great convenience is brought to users.
In conclusion, the system can have a whole set of big data system without downloading and installing any program by a user, and is simple to use; the integration of various types of data is supported, and the user data can be docked only by simply configuring basic information, so that the user data is more simply docked with a large data platform; the system can realize complex data integration, data processing, data analysis, data mining and multi-terminal visualization by clicking, can realize data analysis without any technical background, and is simple to operate.
The visualized page can be displayed in a WEB page, an enterprise large screen and an app, and data in the visualized page is calculated in real time, so that the instantaneity of the data is ensured; the user can self-define the intelligent report, the platform calculates the data and automatically fills the report, and the report is distributed according to the setting of the user.
The system can define and monitor the quality of the access data, manage the data through a full value chain, can give an alarm in various modes such as mails when the data quality does not reach the standard, and can be selectively processed or ignored by a user; the system encrypts and stores the data, and various backup mechanisms are adopted to ensure the safety of the data and the safety of the system; data can be shared among users, and the platform provides shared data such as population, economy, geography, weather and the like.
The foregoing is an exemplary embodiment of the present disclosure, and the scope of the present disclosure is defined by the claims and their equivalents.
Claims (15)
1. A cloud big data system, comprising:
the data access module accesses a multi-type data source D and comprises:
the first type selection unit is used for dividing the types of the accessed data sources D to obtain the parent types of the data sources D;
the second type selection unit is used for dividing the father type to obtain the subtype of the data source D;
a first setting unit that performs first setting on the data source D according to the subtype;
the verification unit is used for verifying the data source D which completes the first setting, if the verification is passed, the data source D is transferred to the preview unit, and if the verification is not passed, the data source D is transferred to the first setting unit;
the previewing unit previews the verified data source D;
a third setting unit, after the preview is finished, performing third setting on the data source D to obtain a data source
Submitting the data source
；
A data processing module for processing the dataSource
Carrying out data processing;
a data analysis module for processing the data source
Carrying out data analysis to obtain an analysis result;
and the data application module generates a visual page or an intelligent report according to the analysis result.
2. The cloud big data system of claim 1, wherein the data access module further comprises a second setting unit, the second setting unit performs a second setting on the data source D after the preview is completed, and the data source D is then subjected to a third setting after the second setting is completed.
3. The cloud big data system of claim 2, wherein the first setup unit comprises a format setup block and a flow setup block; the second setting unit comprises an attribute filtering setting block, an attribute renaming setting block, an attribute format conversion setting block, an attribute expansion setting block and an attribute decryption setting block; the third setting unit sets a block for field information, and the field information includes a data source name, an import mode, and an update period.
4. The cloud big data system of claim 3, wherein the import mode comprises full-size import and incremental import, and the incremental import comprises timed import and real-time import.
5. The cloud big data system of claim 4, wherein the data processing modes of the data processing module include data cleaning, data screening, data filtering, multiple data transformation, data integration, and data reduction.
6. The cloud big data system of claim 5, wherein the data analysis module comprises:
the analysis unit is used for carrying out data statistics, data classification, data regression and data clustering on the data;
the mining unit is used for mining the data to acquire useful information;
the visual page comprises a WEB page, a mobile phone APP page and a large screen, and the large screen comprises an LED large screen and a television large screen.
7. The cloud big data system of claim 6, further comprising a data sharing module and a security management module, wherein the data sharing module is used for sharing data, and the security management module is used for performing security management on the cloud big data system and the data.
8. The cloud big data system of any of claims 1-7, wherein the parent types of the data sources comprise text data, relational data, non-relational data, big data platform data, message queue databases, Internet of things device protocol data, API data, and public data.
9. A construction method of a cloud big data system is characterized by comprising the following steps:
accessing a multi-type data source D;
dividing the types of the accessed data sources D to obtain the parent types of the data sources D;
dividing the father type to obtain a subtype of the data source D;
performing first setting on the data source D according to the subtype;
verifying the data source D which completes the first setting, previewing if the verification is passed, and returning to the first setting if the verification is not passed;
previewing the data source D passing the verification;
after the preview is finished, carrying out third setting on the data source D to obtain a data source, and submitting the data source;
for the data source
Carrying out data processing;
the data source after data processing
Carrying out data analysis to obtain an analysis result;
and generating a visual page or an intelligent report according to the analysis result.
10. The cloud big data system construction method of claim 9, wherein the data source access process further comprises a second setting, after the preview is completed, the second setting is performed on the data source D, and after the second setting is completed, the third setting is performed on the data source D.
11. The cloud big data system construction method of claim 10, wherein the first setting comprises a format setting and a process setting; the second setting comprises attribute filtering, attribute renaming, attribute format conversion, attribute expansion and attribute decryption; the third setting is set as field information, and the field information comprises a data source name, an importing mode and an updating period.
12. The cloud big data system construction method according to claim 11, wherein the import mode includes full-scale import and incremental import, and the incremental import includes timed import and real-time import.
13. The method for constructing the cloud big data system according to claim 12, wherein the data processing manner includes data cleaning, data filtering, multiple data transformation, data integration, and data reduction.
14. The cloud big data system construction method of claim 13, wherein the data analysis comprises:
carrying out data statistics, data classification, data regression and data clustering on the data;
mining data to obtain useful information;
the visual page comprises a WEB page, a mobile phone APP page and a large screen, and the large screen comprises an LED large screen and a television large screen.
15. The cloud big data system construction method of any one of claims 9 to 14, wherein the parent types of data sources include text data, relational data, non-relational data, big data platform data, message queue data, internet of things device protocol data, API data, and public data.
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