CN113468257A - Data quality monitoring method and device based on data warehouse - Google Patents
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Abstract
Embodiments of the present disclosure provide data warehouse-based data quality monitoring methods, apparatuses, devices, and computer-readable storage media. The method comprises the following steps: monitoring query requests for target data in a data warehouse; if the target data aimed at by the query request is the quality suspicious data, obtaining a blood relationship directed graph corresponding to the quality suspicious data; and tracing the suspicious source corresponding to the quality suspicious data according to the blood relationship directed graph. In this way, the suspicious source corresponding to the quality suspicious data can be traced in a targeted manner, so that the data source with problems can be quickly positioned, and the problem processing speed can be improved.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of data processing, and more particularly, to a data warehouse-based data quality monitoring method, apparatus, device, and computer-readable storage medium.
Background
Currently, enterprise data management is becoming more and more important, and in the enterprise datamation process, a data warehouse, a mart, or a data lake is usually established, and the process involves an Extract-Transform-Load (ETL-Load) data processing flow. However, when data has problems, how to quickly locate the data source with problems becomes another problem to be solved.
Disclosure of Invention
According to an embodiment of the present disclosure, a data quality monitoring scheme based on a data warehouse is provided.
In a first aspect of the disclosure, a data warehouse-based data quality monitoring method is provided. The method comprises the following steps:
monitoring query requests for target data in a data warehouse;
if the target data aimed at by the query request is the quality suspicious data, obtaining a blood relationship directed graph corresponding to the quality suspicious data;
and tracing the suspicious source corresponding to the quality suspicious data according to the blood relationship directed graph.
The above-described aspect and any possible implementation further provide an implementation in which the quality-suspect data is determined by:
determining whether target data stored in the data warehouse is suspicious by monitoring the quality of the target data;
the quality monitoring comprises:
data cross validation, data transverse comparison and data longitudinal comparison.
The above-described aspects and any possible implementations further provide an implementation, and the method further includes:
obtaining target data and intermediate data associated with the ETL processing process after ETL processing is carried out on source data;
generating a data kindred graph from the source data, the target data, the ETL processing procedure, and the intermediate data, wherein the source data, the target data, and the intermediate data represent data nodes in the data kindred graph, and the data processing procedure represents edges between the data nodes in the data kindred graph;
and storing the data blood margin map.
As to the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, where if the target data targeted by the query request is quality suspicious data, obtaining a blood relationship directed graph corresponding to the quality suspicious data includes:
inquiring corresponding ETL processing process, intermediate data and source data from the data blood margin map according to the quality suspicious data;
and generating the blood relationship directed graph according to the quality suspicious data, the corresponding ETL processing process, the intermediate data and the source data.
The above-described aspects and any possible implementations further provide an implementation, and the method further includes:
and highlighting the blood relationship directed graph.
The above-described aspect and any possible implementation further provide an implementation, where the source data includes a plurality of data packets;
acquiring a data relation among a plurality of source data; the data relationships include: a dependency or mutual exclusion relationship;
generating a data blood margin map from the source data, the target data, the ETL process, and the intermediate data, comprising:
generating the data consanguinity map according to the source data, the target data, the ETL process, the intermediate data, and the data relationships.
The above-described aspects and any possible implementations further provide an implementation, and the method further includes:
displaying the data blood relationship map;
processing a target object in the data limbus atlas when a trigger operation is received for the target object, the trigger operation comprising at least one of: highlighting operations, deleting operations, and moving operations, the target object comprising a data node or an edge.
In a second aspect of the present disclosure, a data quality monitoring apparatus based on a data warehouse is provided. The device includes:
the monitoring module is used for monitoring a query request aiming at target data in the data warehouse;
the obtaining module is used for obtaining a blood relation directed graph corresponding to the quality suspicious data if the target data aimed at by the query request is the quality suspicious data;
and the tracing module is used for tracing the suspicious source corresponding to the quality suspicious data according to the blood relationship directed graph.
In a third aspect of the disclosure, an electronic device is provided. The electronic device includes: a memory having a computer program stored thereon and a processor implementing the method as described above when executing the program.
In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the method as according to the first and/or second aspect of the present disclosure.
The technical scheme of the present disclosure can achieve the following technical effects:
after the quality suspicious data in the data warehouse are inquired, the bloody border relation directed graph corresponding to the quality suspicious data can be automatically acquired, and then the suspicious source corresponding to the quality suspicious data is pertinently traced based on the bloody border relation directed graph, so that the data source with problems is quickly positioned, and the problem processing speed is favorably improved.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
FIG. 1 shows a flow diagram of a data warehouse-based data quality monitoring method according to an embodiment of the present disclosure;
figure 2 shows a schematic diagram of a data consanguinity map according to an embodiment of the present disclosure;
FIG. 3 illustrates a block diagram of a data warehouse-based data quality monitoring apparatus, in accordance with an embodiment of the present disclosure;
FIG. 4 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
In the method, if the target data in the queried data warehouse is the data with suspicious quality, the bloody border relationship directed graph corresponding to the data with suspicious quality is automatically acquired, and then the suspicious source corresponding to the data with suspicious quality is pertinently traced based on the bloody border relationship directed graph, so that the data source with problems is quickly positioned, and the problem processing speed is favorably improved.
FIG. 1 shows a flow diagram of a data warehouse-based data quality monitoring method 100 according to an embodiment of the present disclosure. The method 100 comprises:
and step 130, tracing a suspicious source corresponding to the quality suspicious data according to the blood relationship directed graph.
After receiving a query request for target data in a data warehouse, if the queried target data is suspicious data in quality, automatically acquiring a blood relationship directed graph corresponding to the suspicious data in quality, and then tracing a suspicious source corresponding to the suspicious data in quality in a targeted manner based on the blood relationship directed graph.
In one embodiment, the quality suspect data is determined by:
determining whether target data stored in the data warehouse is suspicious by monitoring the quality of the target data;
the quality monitoring comprises:
data cross validation, data transverse comparison and data longitudinal comparison.
By performing at least one of quality monitoring of data cross validation, transverse comparison and longitudinal comparison on target data in the data warehouse, whether the target data is suspicious data can be accurately determined.
For example: when the target data is the per-person income, cross verification can be carried out by utilizing the user number and the total income, namely, a value obtained by dividing the total income by the user number is compared with the per-person income, and if the error is in an allowable range, the per-person income is proved to have no problem. Or
And comparing the per-capita income transversely along a time axis, judging whether the variation range of the income is within a threshold range (such as 5 percent), and if so, indicating that the per-capita income has no problem. Or alternatively
And comparing the per-capita income of the current city with the per-capita income of other cities longitudinally, and judging whether the variation amplitude of the per-capita income of the current city compared with that of the other cities is within a reasonable range, wherein if the variation amplitude is within the reasonable range, the per-capita income of the current city is not problematic.
In one embodiment, the method further comprises:
obtaining target data and intermediate data associated with the ETL processing process after ETL processing is carried out on source data;
generating a data blood-border map according to the source data, the target data, the ETL processing procedure and the intermediate data, wherein the source data, the target data and the intermediate data represent data nodes in the data blood-border map, and the data processing procedure represents edges between the data nodes in the data blood-border map, as shown in fig. 2, that is, each data in the data blood-border map is a node, and the data processing ETL procedure is an edge;
and storing the data blood margin map. The suspicious source may be any one of source data, the target data, the ETL process, and the intermediate data, and may be embodied in the form of a field, a table, a processing logic, and the like.
After ETL processing is performed on source data, intermediate data obtained after the ETL processing and finally output data, namely target data, can be obtained, then the source data, the intermediate data, the ETL processing process and the target data are correlated, so that a data blood margin map is generated, and the data blood margin map is cached in Redis (data structure server) or is stored in a data warehouse persistently so as to be used later.
The data blood margin map essentially describes the association between various data and between data and data processing.
In addition, the source data is massive, the corresponding intermediate data, the target data and the ETL processing process are also massive, and the relationship among the data blood-related maps obtained in the way is also complicated, so that the problem data can be effectively traced, and the source, namely the suspicious source, can be conveniently traced.
The data kindred maps will be further explained below:
for example: in a relational database, each cell in a data table may be treated as a data unit.
In the front-end presentation, the report may be treated as a data unit.
And (3) data processing tasks: is a process of data processing. And loading data from a source, then processing, and finally outputting a processing result to a target.
Blood margin analysis: the data flow direction of all ETL processing tasks can be analyzed according to the logic of the data processing tasks (and the input source and output target of the tasks). The analyzed result can form a data blood-margin map, wherein the data units are used as nodes of the data blood-margin map, and the ETL processing tasks are used as edges of the data blood-margin map (each task can be the edges of a plurality of directed graphs in the data blood-margin map).
In an embodiment, if the target data targeted by the query request is quality suspicious data, acquiring a blood relationship directed graph corresponding to the quality suspicious data includes:
inquiring corresponding ETL processing process, intermediate data and source data from the data blood margin map according to the quality suspicious data;
and generating the blood relationship directed graph according to the quality suspicious data, the corresponding ETL processing process, the intermediate data and the source data.
Because the data blood-border map reflects the incidence relation among various data, an ETL data processing process corresponding to the quality suspicious data, corresponding intermediate data and corresponding source data can be inquired from the data blood-border map according to the quality suspicious data, then a blood-border relation directed graph related to the data and the ETL processing process can be generated according to the ETL processing process and the flow direction of the data, namely a data chain where the quality suspicious data is located is obtained, then the problem data is traced back through the blood-border relation directed graph, and therefore the efficiency of searching suspicious sources of the data with problems can be improved.
In addition, in the process of tracing the suspicious source based on the blood relationship directed graph, that is, further performing quality monitoring on each data in the blood relationship directed graph, and performing manual or automatic verification on the data processing process to determine a specific suspicious source, the suspicious source may be data which is in error in the blood relationship directed graph or an ETL process which is in error.
In one embodiment, the method further comprises:
and highlighting the blood relationship directed graph.
Through highlighting the blood relationship directed graph, a data chain with problems in the data blood relationship graph can be prompted to a user in a striking manner, so that a data source can be traced more quickly.
In one embodiment, the source data includes a plurality; the source data comprises a plurality of source data which can come from different databases, and the richer the source data is, the richer and more comprehensive the data blood margin map is.
As shown in fig. 2, if the data with questionable quality in the data blood-level map of fig. 2 is report 1, and the traced blood-level relationship directed graph is "data source 1-ETL 1-report 1", then "data source 1-ETL 1-report 1" is highlighted.
Acquiring a data relation among a plurality of source data; the data relationships include: a dependency or mutual exclusion relationship;
generating a data blood margin map from the source data, the target data, the ETL process, and the intermediate data, comprising:
generating the data consanguinity map according to the source data, the target data, the ETL process, the intermediate data, and the data relationships.
By acquiring the data relationship of a plurality of source data, the data blood margin map with comprehensive relationship and complicated load can be automatically generated according to the data relationship among a plurality of source data, target data, the ETL processing process and intermediate data, so that the problem data can be effectively traced.
In one embodiment, the method further comprises:
displaying the data blood relationship map;
processing a target object in the data limbus atlas when a trigger operation is received for the target object, the trigger operation comprising at least one of: highlighting operations, deleting operations, and moving operations, the target object comprising a data node or an edge.
After the data blood-margin graph is displayed, if the triggering operation of any target object in the data blood-margin graph is received, the target object is automatically processed, so that a user can conveniently operate the target object, for example, a certain data node or edge in the data blood-margin graph is highlighted, or a certain data node or edge in the data blood-margin graph is deleted, or a certain data node or edge in the data blood-margin graph is moved, and therefore, the data blood-margin graph is favorably changed.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
Fig. 3 illustrates a block diagram of a data warehouse-based data quality monitoring apparatus 500 according to an embodiment of the present disclosure. The apparatus 300 may comprise:
a monitoring module 310 for monitoring query requests for target data in the data warehouse;
an obtaining module 320, configured to obtain a blood relationship directed graph corresponding to the quality suspicious data if the target data targeted by the query request is the quality suspicious data;
and a tracing module 330, configured to trace the suspicious source corresponding to the quality suspicious data according to the blood relationship directed graph.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
FIG. 4 shows a schematic block diagram of an electronic device 400 that may be used to implement embodiments of the present disclosure. The apparatus 400 may be used to implement the data warehouse-based data quality monitoring device 100 of fig. 1. As shown, the device 400 includes a CPU401 that can perform various appropriate actions and processes according to computer program instructions stored in a ROM402 or loaded from a storage unit 408 into a RAM 403. In the RAM403, various programs and data required for the operation of the device 400 can also be stored. The CPU401, ROM402, and RAM403 are connected to each other via a bus 404. An I/O interface 405 is also connected to bus 404.
A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM, an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A data quality monitoring method based on a data warehouse is characterized by comprising the following steps:
monitoring query requests for target data in a data warehouse;
if the target data aimed at by the query request is the quality suspicious data, obtaining a blood relationship directed graph corresponding to the quality suspicious data;
and tracing the suspicious source corresponding to the quality suspicious data according to the blood relationship directed graph.
2. The method of claim 1, wherein the quality suspect data is determined by:
determining whether target data stored in the data warehouse is suspicious by monitoring the quality of the target data;
the quality monitoring comprises:
data cross validation, data transverse comparison and data longitudinal comparison.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
obtaining target data and intermediate data associated with the ETL processing process after ETL processing is carried out on source data;
generating a data kindred graph from the source data, the target data, the ETL processing procedure, and the intermediate data, wherein the source data, the target data, and the intermediate data represent data nodes in the data kindred graph, and the data processing procedure represents edges between the data nodes in the data kindred graph;
and storing the data blood margin map.
4. The method of claim 3,
if the target data targeted by the query request is the quality suspicious data, obtaining a blood relationship directed graph corresponding to the quality suspicious data, including:
inquiring corresponding ETL processing process, intermediate data and source data from the data blood margin map according to the quality suspicious data;
and generating the blood relationship directed graph according to the quality suspicious data, the corresponding ETL processing process, the intermediate data and the source data.
5. The method of claim 4, further comprising:
and highlighting the blood relationship directed graph.
6. The method of claim 3,
the source data comprises a plurality of data;
acquiring a data relation among a plurality of source data; the data relationships include: a dependency or mutual exclusion relationship;
generating a data blood margin map from the source data, the target data, the ETL process, and the intermediate data, comprising:
generating the data consanguinity map according to the source data, the target data, the ETL process, the intermediate data, and the data relationships.
7. The method of claim 3, further comprising:
displaying the data blood relationship map;
processing a target object in the data limbus atlas when a trigger operation is received for the target object, the trigger operation comprising at least one of: highlighting operations, deleting operations, and moving operations, the target object comprising a data node or an edge.
8. A data quality monitoring apparatus based on a data warehouse, comprising:
the monitoring module is used for monitoring a query request aiming at target data in the data warehouse;
the obtaining module is used for obtaining a blood relation directed graph corresponding to the quality suspicious data if the target data aimed at by the query request is the quality suspicious data;
and the tracing module is used for tracing the suspicious source corresponding to the quality suspicious data according to the blood relationship directed graph.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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| CN113032579A (en) * | 2021-03-25 | 2021-06-25 | 中国建设银行股份有限公司 | Metadata blood margin analysis method and device, electronic equipment and medium |
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