CN112434036A - Account management system data processing method - Google Patents
Account management system data processing method Download PDFInfo
- Publication number
- CN112434036A CN112434036A CN202011327688.6A CN202011327688A CN112434036A CN 112434036 A CN112434036 A CN 112434036A CN 202011327688 A CN202011327688 A CN 202011327688A CN 112434036 A CN112434036 A CN 112434036A
- Authority
- CN
- China
- Prior art keywords
- data
- processing
- management system
- account management
- oracle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 14
- 238000012545 processing Methods 0.000 claims abstract description 75
- 238000000034 method Methods 0.000 claims abstract description 49
- 230000008569 process Effects 0.000 claims abstract description 31
- 238000005192 partition Methods 0.000 claims abstract description 13
- 230000007246 mechanism Effects 0.000 claims abstract description 10
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 238000004140 cleaning Methods 0.000 claims description 5
- 238000007405 data analysis Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 abstract description 3
- 230000008901 benefit Effects 0.000 abstract description 2
- 238000012423 maintenance Methods 0.000 abstract description 2
- 238000007726 management method Methods 0.000 description 38
- 238000004458 analytical method Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 5
- 230000003203 everyday effect Effects 0.000 description 5
- 230000002354 daily effect Effects 0.000 description 3
- 238000009960 carding Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000012634 fragment Substances 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000013024 troubleshooting Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2453—Query optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/52—Program synchronisation; Mutual exclusion, e.g. by means of semaphores
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Computational Linguistics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention relates to a processing method of account management system data, which takes an all-in-one machine as a database server, carries out layered processing of a warehousing layer, a basic layer and an application layer on account management system data in sequence by utilizing an Oracle storage process, realizes data slice storage of the account management system data processed by the application layer in an Oracle partition table mode according to days, divides the data into a plurality of tasks after the layered processing, automatically controls a concurrency mechanism of the plurality of tasks, and realizes multi-task concurrency control. Compared with the prior art, the invention has the advantages of high timeliness, simple construction and maintenance, maximum resource utilization and the like.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method for processing data of an account management system.
Background
In the financial industry, an account management system needs to update the latest information of all accounts every day, and process and generate decision variables of a client layer, an account layer and a transaction layer for batch and real-time decision use of various businesses. In order to carry out quota adjustment or collection urging decision on more than 3000 ten thousand customers, basic information, account information, card information, bill information, human credit and other multi-dimensional information of all the customers need to be processed every day, the data processing amount in the process is large, and extremely large storage space and processing capacity are needed to meet the enterprise-level application requirements.
On-line analysis Processing (OLAP) is a quick analysis technology for sharing multidimensional information, and a multidimensional database technology is utilized to enable users to observe data from different angles; the OLAP can support complex analysis operation, focuses on decision support for managers, can meet the requirement that analysts quickly and flexibly perform complex query of large data complex quantity, and presents query results in an intuitive and understandable form to assist decision. Spark SQL is the current mainstream OLAP online analysis processing technology, however, in the aspect of dealing with short-time and large-scale data processing, such as completing 10TB data processing within 2 hours, the problems of high cost and large difficulty in realizing part of analysis scenes exist, that is, in the current data processing process of an account management system, the timeliness of the OLAP technology is difficult to guarantee and the construction is complex; in addition, in the processing process, server alarm is easy to occur due to overload processing, the processing efficiency of the multidimensional information data cannot be ensured, and resources cannot be fully utilized.
In addition, the OLAP batch analysis refers to processing various variables from multiple dimensions of customers, accounts, transactions, etc. for subsequent decision-making, such as average number of transfers of customers in near 6 months, and the account management system may process hundreds of such variables for batch decision-making for over 3000 thousands of customers per day, which is the OLAP batch analysis. The variables are processed and stored in the account management system, and if a customer with a similar quota requests a transaction, the stored variables can be immediately acquired to make a real-time decision, namely, an OLTP online transaction. At present, the big data processing and decision tasks of the two variables need to be realized in different systems, cannot be realized based on the same system, and are high in cost and complex in operation.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a method for processing account management system data, which can exert the high IO characteristic of an all-in-one machine and the mature analysis function of Oracle on the other hand by using a data processing architecture of the all-in-one machine combined with an Oracle database, and can realize the high-performance processing of big data most quickly and completely on the premise of meeting the requirement of service processing timeliness.
The purpose of the invention can be realized by the following technical scheme:
a processing method of account management system data uses an all-in-one machine as a database server, carries out layering processing of a warehousing layer, a base layer and an application layer on the account management system data in sequence by utilizing an Oracle storage process, realizes data slice storage of the account management system data processed by the application layer in an Oracle partition table mode according to days, splits the data into a plurality of tasks after layering processing, automatically controls a concurrency mechanism for the plurality of tasks, and realizes multitask concurrency control.
Further, seven days of data slice storage is provided for the same data source in an Oracle partition table manner.
And after the layered processing, performing parallel processing on the split tasks by using a PDML parallel processing mechanism of Oracle. After the layered processing, the data is divided into more than 400 tasks, and parallel processing is carried out by utilizing a PDML parallel processing mechanism of Oracle.
After the data is split into a plurality of tasks, the account management system sets the maximum concurrency number for each task, when the tasks are scheduled, automatic control is carried out by calculating whether the newly added tasks exceed the maximum concurrency number allowed by the system, if the newly added tasks do not exceed the maximum concurrency number allowed by the system, the tasks are allowed to run, otherwise, the tasks are allowed to wait.
The processing process of the warehousing layer is to carry out cleaning conversion processing on various source data of the account management system data; the cleaning conversion processing comprises null value processing and data format specification processing.
The processing process of the basic layer is merging processing of data.
And the processing process of the application layer is to process different dimensionality summary information for the decision-making fields related to the account management data.
The processing method of the account management system data realizes batch data analysis through the all-in-one machine database, carries out batch decision, inquires batch information from the all-in-one machine database by connecting with a common server, carries out real-time decision by combining with the information inquired in real time, and stores the transaction information in the real-time database.
Compared with the prior art, the processing method of the account management system data provided by the invention at least has the following beneficial effects:
1) the data of the account management system is layered by taking the all-in-one machine as a carrier through multi-task concurrent control, and is divided into pieces on a daily basis, so that the timeliness requirements of different application fields can be supported, the processing of more than 10TB data can be completed within 2 hours, and the timeliness is higher;
2) the big data application supporting more than 3000 thousands of clients can be constructed only by the all-in-one machine and the Oracle database, the construction and maintenance are simple, and the cost is low;
3) the data of the whole account management system is divided according to a warehousing layer, a base layer and an application layer, so that the processing logic of each layer can be quickly positioned, the problems of data carding and troubleshooting are facilitated, the traceability of the data can be ensured, the repeated work of format conversion, code value mapping and the like caused by directly processing the original data can be avoided, and the resources are utilized to the maximum extent;
4) the method adopts a parallel technology to divide the data of the account management system into a plurality of tasks, simultaneously starts a plurality of processes to process the plurality of tasks in parallel, and the concurrent processes can be uniformly managed by one process called a concurrent coordination process, so that the data processing capability and the data processing efficiency can be greatly improved, and the I/O and CPU resources of the all-in-one machine can be utilized to the maximum extent by automatically controlling a concurrent mechanism;
5) the method comprises the steps that data are divided into pieces on a daily basis for application layer data of an account management system in an Oracle partition table mode, each partition represents data of one day, if one query only uses data of one partition in one partition table, Oracle only scans the used data of the partition when executing the query, data of other partitions are not scanned, and different fields can respectively obtain the data so as to meet different data timeliness requirements;
6) the account management system sets the maximum concurrency number for each task, and calculates whether the newly added task exceeds the maximum concurrency number allowed by the system during task scheduling so as to avoid server alarm or unpredictable risk caused by overload;
7) based on the data processing method, the account management system can use the all-in-one machine database to realize batch data analysis and batch decision, can use the common server to inquire batch information from the all-in-one machine database at the same time, and can combine the information inquired in real time to make real-time decision, so that the transaction information falls into the real-time database to be stored.
Drawings
FIG. 1 is a schematic diagram of a hierarchical processing method of data of an account management system in an embodiment;
fig. 2 is a database network architecture diagram of a processing method of account management system data in the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
The invention discloses a processing method of account management system data, which takes an all-in-one machine as a database server, carries out data layering processing on the account management system data under multitask concurrent control by utilizing an Oracle database, and carries out daily slicing processing on the data in different application fields so as to finish the data processing flow of thousands of variables. The all-in-one machine is a hardware server, has strong computing capability and high internal bandwidth, and can expand computing nodes and storage nodes. The invention runs the Oracle database on the integrated machine, and can construct a high-performance database server by combining the Oracle database and the Oracle database, thereby realizing the storage and processing of big data. Because the OLAP needs to have high I/O and large storage as a server, the all-in-one machine has high computing and storage reliability, and the network interaction capacity between the computing and the storage is as high as 56Gbps, the all-in-one machine can effectively ensure the bandwidth requirement required by the OLAP system.
The invention takes an all-in-one machine as a carrier of an account management system, firstly, the data of the account management system sequentially realizes the layered processing of a warehousing layer, a base layer and an application layer by utilizing an Oracle storage process, and the warehousing layer is used for cleaning and converting various source data, such as null value processing and data format specification; the basic layer is used for merging data, such as merging the information of a client into the same table; the application layer is oriented to various decision fields such as quota adjustment, collection urging and the like, and processing summarized information with different dimensions, such as cash withdrawal amount of each account monthly. The advantage of processing the account management system data in a layering manner by utilizing an Oracle storage process is that the processing logic of each layer can be quickly positioned, and the data carding and troubleshooting are facilitated; meanwhile, when a plurality of variables are from the same data source, repeated data cleaning conversion is not needed. The method can ensure the traceability of data, avoid repeated work such as format conversion, code value mapping and the like caused by directly processing the original data, and utilize resources to the maximum extent.
In the application layer processing process, because each data corresponds to different decision-making fields (application fields), different application fields have different requirements on the timeliness of the data, some application fields need to generate data on a day of T +1(T is an account single day), and some application fields can generate data on a day of T +2, for example, transaction data can be collected every day, early warning needs the transaction data of the previous day, and adjustment needs the transaction data of the current day; on the premise that different application fields have different data timeliness requirements, the system needs to ensure the consistency of data time points and date inconsistency when the data are not acquired, for example, the data of T-1 day is acquired, and the data of T-2 day is acquired.
Therefore, the account management system data processed by the application layer is segmented according to days (days), namely the application layer data of the account management system is stored in a data slice mode in an Oracle partition table, each partition represents data of one day, and different fields can respectively obtain the data needed so as to meet different data timeliness requirements. As a preferred scheme, the data fragments of 7 days can be provided for the same data source, and the application can take respective fragments according to the needs, so that the flexibility of the upper-layer application is improved.
After the account management system data is processed hierarchically, the processed data is split into more than 400 tasks, and the split more than 400 tasks are processed in Parallel by using a PDML (Parallel DML) Parallel processing mechanism of Oracle, during the PDML, Oracle can use a plurality of Parallel execution servers (i.e. concurrent processes) to execute a plurality of tasks simultaneously, and each task (concurrent process) is an independent transaction, and the transactions are either submitted by the concurrent coordination process or are rollback, and the concurrent processes are called as Parallel execution servers and can be uniformly managed by one process called as the concurrent coordination process, so that the Parallel processing capability can be improved.
In the above parallel processing, the number of concurrencies needs to be controlled in order to avoid server alarms or unpredictable risks caused by overload. The account management system calculates whether the newly added task exceeds the maximum allowable concurrency number of the system during task scheduling by setting the maximum allowable concurrency number for each task, and if not, the task is allowed to run, otherwise, the task waits. By automatically controlling the concurrency mechanism, the I/O and CPU resources of the all-in-one machine can be utilized to the maximum extent.
Based on the processing method, the account management system can use the all-in-one machine database to realize batch data analysis and batch decision, can use a common server to inquire batch information from the all-in-one machine database, can make real-time decision by combining the information inquired in real time, and can store the transaction information in the real-time database. I.e. the account management system can now implement these two tasks, which were previously implemented in different systems, in one system.
The database network architecture of the processing method of the account management system data of the invention is shown in fig. 2 and comprises a high-distribution real-time main database, a high-distribution real-time standby database, an all-in-one machine batch main database and an all-in-one machine batch standby database. And the all-in-one machine batch master database realizes batch data analysis on the account management system data according to the processing method of the account management system data and carries out batch decision. The high-distribution real-time master database inquires batch information from the all-in-one machine batch master database, carries out real-time decision by combining the real-time inquired information, and sends the obtained real-time decision information and the real-time transaction information to the high-distribution real-time master database for storage.
The all-in-one machine batch main database stores data processed by all clients every day, the database is possibly down or is unusable due to other reasons, at the moment, the database can be automatically switched to another database, the database is a standby database, and the standby database automatically synchronizes the data of the main database. The real-time database stores real-time transaction data, and the batch database stores data of all dimensions of clients, accounts and transactions for decision making every day. The account management system has two applications, one is that the batch decision is connected with the batch database, and the other is that the real-time decision is to acquire data from the batch database and also acquire required data from the real-time database to complete the real-time decision together.
The whole framework is simple and easy to implement, meets the requirement of high availability, and can support batch and real-time decision making of over 3000 thousands of clients. High availability can be satisfied by using the main and standby database architectures.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A processing method of account management system data is characterized in that an all-in-one machine is used as a database server, the hierarchical processing of a warehousing layer, a basic layer and an application layer is sequentially carried out on account management system data by utilizing an Oracle storage process, data slice storage is realized on the account management system data processed by the application layer in an Oracle partition table mode according to days, after the hierarchical processing, the data are divided into a plurality of tasks, a concurrency mechanism is automatically controlled on the tasks, and multi-task concurrency control is realized.
2. The method for processing the data of the account management system according to claim 1, wherein after the hierarchical processing, the split tasks are processed in parallel by using a PDML parallel processing mechanism of Oracle.
3. The method for processing account management system data according to claim 2, wherein after the hierarchical processing, the data is divided into 400 or more tasks, and the parallel processing is performed by using a PDML parallel processing mechanism of Oracle.
4. The method for processing the data of the account management system according to claim 1, wherein after the data is divided into a plurality of tasks, the account management system sets a maximum concurrency number for each task, and performs automatic control by calculating whether the newly added task exceeds the maximum concurrency number allowed by the system during task scheduling, and if the newly added task does not exceed the maximum concurrency number allowed by the system, the task is allowed to run, otherwise, the task is allowed to wait.
5. The method for processing account management system data according to claim 1, wherein the processing procedure of the warehousing layer is to perform cleaning conversion processing of various types of source data on the account management system data.
6. The method for processing data of an account management system according to claim 1, wherein the processing procedure of the base layer is a merging process of data.
7. The method for processing the account management system data according to claim 1, wherein the processing procedure of the application layer is to process summarized information with different dimensions for decision fields related to each account management data.
8. The method for processing data of an account management system according to claim 5, wherein the cleansing conversion process includes a null value process and a data format specification process.
9. The method for processing data of an account management system according to claim 1, wherein the same data source is provided with data slice storage for seven days in an Oracle partition table manner.
10. The method for processing the account management system data according to claim 1, wherein the method is characterized in that batch data analysis is realized through an all-in-one machine database, batch decision is made, batch information is inquired from the all-in-one machine database by connecting a common server, real-time decision is made by combining with the information inquired in real time, and transaction information falls into the real-time database for storage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011327688.6A CN112434036A (en) | 2020-11-24 | 2020-11-24 | Account management system data processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011327688.6A CN112434036A (en) | 2020-11-24 | 2020-11-24 | Account management system data processing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112434036A true CN112434036A (en) | 2021-03-02 |
Family
ID=74693910
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011327688.6A Pending CN112434036A (en) | 2020-11-24 | 2020-11-24 | Account management system data processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112434036A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114218173A (en) * | 2021-12-30 | 2022-03-22 | 北京宇信科技集团股份有限公司 | Batch processing system, processing method, medium and equipment for account-transfer transaction files |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101620600A (en) * | 2008-06-30 | 2010-01-06 | 上海全成通信技术有限公司 | Method for processing mass data |
| CN103064890A (en) * | 2012-12-11 | 2013-04-24 | 泉州豪杰信息科技发展有限公司 | Global position system (GPS) mass data processing method |
| CN103473260A (en) * | 2013-06-25 | 2013-12-25 | 北京控制工程研究所 | Concurrency OLAP (On-Line Analytical Processing)-oriented test data hierarchy cluster query processing system and method |
| CN104424229A (en) * | 2013-08-26 | 2015-03-18 | 腾讯科技（深圳）有限公司 | Calculating method and system for multi-dimensional division |
| CN105843959A (en) * | 2016-04-18 | 2016-08-10 | 中国建设银行股份有限公司 | Bonus point calculation method and system based on processing of big data |
| US20170293530A1 (en) * | 2016-04-07 | 2017-10-12 | International Business Machines Corporation | Providing snapshot isolation to a database management system |
| CN107423390A (en) * | 2017-07-21 | 2017-12-01 | 上海德拓信息技术股份有限公司 | A kind of real time data synchronization algorithm based on inside OLTP OLAP mixed relationship type Database Systems |
| CN110019251A (en) * | 2019-03-22 | 2019-07-16 | 深圳市腾讯计算机系统有限公司 | A kind of data processing system, method and apparatus |
| CN111930817A (en) * | 2020-07-28 | 2020-11-13 | 银盛支付服务股份有限公司 | Big data-based distributed unstructured database correlation query method |
-
2020
- 2020-11-24 CN CN202011327688.6A patent/CN112434036A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101620600A (en) * | 2008-06-30 | 2010-01-06 | 上海全成通信技术有限公司 | Method for processing mass data |
| CN103064890A (en) * | 2012-12-11 | 2013-04-24 | 泉州豪杰信息科技发展有限公司 | Global position system (GPS) mass data processing method |
| CN103473260A (en) * | 2013-06-25 | 2013-12-25 | 北京控制工程研究所 | Concurrency OLAP (On-Line Analytical Processing)-oriented test data hierarchy cluster query processing system and method |
| CN104424229A (en) * | 2013-08-26 | 2015-03-18 | 腾讯科技（深圳）有限公司 | Calculating method and system for multi-dimensional division |
| US20170293530A1 (en) * | 2016-04-07 | 2017-10-12 | International Business Machines Corporation | Providing snapshot isolation to a database management system |
| CN105843959A (en) * | 2016-04-18 | 2016-08-10 | 中国建设银行股份有限公司 | Bonus point calculation method and system based on processing of big data |
| CN107423390A (en) * | 2017-07-21 | 2017-12-01 | 上海德拓信息技术股份有限公司 | A kind of real time data synchronization algorithm based on inside OLTP OLAP mixed relationship type Database Systems |
| CN110019251A (en) * | 2019-03-22 | 2019-07-16 | 深圳市腾讯计算机系统有限公司 | A kind of data processing system, method and apparatus |
| CN111930817A (en) * | 2020-07-28 | 2020-11-13 | 银盛支付服务股份有限公司 | Big data-based distributed unstructured database correlation query method |
Non-Patent Citations (2)
| Title |
|---|
| 张嫚丽: "投融资企业Oracle EBS系统设计与实施", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
| 步腾跃: "商业银行大数据分析平台的设计与实现", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114218173A (en) * | 2021-12-30 | 2022-03-22 | 北京宇信科技集团股份有限公司 | Batch processing system, processing method, medium and equipment for account-transfer transaction files |
| CN114218173B (en) * | 2021-12-30 | 2022-10-28 | 北京宇信科技集团股份有限公司 | Batch processing system, processing method, medium and equipment for account-transfer transaction files |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110022226B (en) | Object-oriented data acquisition system and acquisition method | |
| CN105049268B (en) | Distributed computing resource distribution system and task processing method | |
| CN111327681A (en) | Cloud computing data platform construction method based on Kubernetes | |
| CN109716320A (en) | Figure for distributed event processing system generates | |
| CN107395669A (en) | A kind of collecting method and system based on the real-time distributed big data of streaming | |
| CN111382150A (en) | Real-time computing method and system based on Flink | |
| CN113129063B (en) | Electric charge calculation issuing method and system based on cloud platform and data center platform | |
| Munar et al. | A big data financial information management architecture for global banking | |
| CN106528853A (en) | Data interaction management device and cross-database data interaction processing device and method | |
| CN113741883B (en) | RPA lightweight data middling station system | |
| CN108334557A (en) | A kind of aggregated data analysis method, device, storage medium and electronic equipment | |
| Tank et al. | Speeding ETL processing in data warehouses using high-performance joins for changed data capture (cdc) | |
| CN111126852A (en) | BI application system based on big data modeling | |
| CN110858197A (en) | Method and device for synchronizing data | |
| CN112286957A (en) | API application method and system of BI system based on structured query language | |
| CN112559634A (en) | Big data management system based on computer cloud computing | |
| CN110442627A (en) | Data transmission method and system between a kind of memory database system and data warehouse | |
| Jiang et al. | Alibaba hologres: A cloud-native service for hybrid serving/analytical processing | |
| US8655920B2 (en) | Report updating based on a restructured report slice | |
| CN112434036A (en) | Account management system data processing method | |
| CN110704442A (en) | Real-time acquisition method and device for big data | |
| Kolchanova et al. | Solving enterprise management problem with cluster technologies and ERP-systems (in the context of capital CSE system) | |
| CN113342826A (en) | Method, storage medium and system for uniformly managing data operations of different data acquisition engines | |
| CN112181972A (en) | Data management method and device based on big data and computer equipment | |
| CN114155076A (en) | Method, device and equipment for checking business data and financial data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210302 |
|
| RJ01 | Rejection of invention patent application after publication |