Data report development method based on combination of preprocessing and real-time query

Abstract

The invention relates to the technical field of data processing, and provides a preprocessing and real-time query combination-based data report development method, which comprises the following steps of: 1) data integration and purification: collecting data from different sources by utilizing a data scheduling technology, performing deep cleaning, and constructing a basic data warehouse; 2) intelligent preprocessing dimension division: according to business requirements, preprocessing the basic data into a list or a statistical abstract of multiple dimensions; 3) constructing a pre-processing data warehouse: organizing the pre-processed data into a form easy to query to form the pre-processing data warehouse; and 4) dynamic parameterization query assembly: receiving a query request through a front-end user interface, dynamically assembling query logic by a back-end system according to request parameters, and extracting required information from the pre-processing data warehouse. According to the method, efficient query performance is kept, and various query requirements can be flexibly met.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data report development method based on the combination of pre-processing and real-time query. Background Art

[0002] With the development of information technology, enterprises have a growing demand for data analysis. However, traditional report development methods face problems of low efficiency and slow response when processing large-scale data sets. Although there are some solutions on the market that try to speed up queries through caching, indexing, etc., they are still insufficient in flexibility and accuracy.

[0003] Currently, full memory database and cache mechanism are often used to speed up query speed. Although this method can improve query efficiency to a certain extent, since all data needs to be loaded into memory, it has extremely high requirements on hardware resources and is difficult to adapt to the situation of rapid growth of data scale. Summary of the invention

[0004] The present invention provides a data report development method based on the combination of pre-processing and real-time query, which can maintain efficient query performance and flexibly respond to various query requirements.

[0005] According to the present invention, a data report development method based on the combination of pre-processing and real-time query includes the following steps:

[0006] 1) Data integration and purification: Use data scheduling technology to collect data from different sources, perform deep cleaning, and build a basic data warehouse;

[0007] 2) Intelligent pre-processing and dimension division: pre-process basic data into lists or statistical summaries of multiple dimensions according to business needs;

[0008] 3) Build a pre-processing data warehouse: organize the pre-processed data into a form that is easy to query, forming a pre-processing data warehouse;

[0009] 4) Dynamic parameterized query assembly: The query request is received through the front-end user interface, and the back-end system dynamically assembles the query logic according to the request parameters to extract the required information from the pre-processed data warehouse.

[0010] Preferably, in step 1), specifically:

[0011] 1.1) Data source identification;

[0012] Data sources include:

[0013] Order system: including order ID, customer ID, order date, product ID, quantity, and unit price fields;

[0014] Inventory system: including product ID, product name, category, inventory, and supplier fields;

[0015] Other data sources: including customer management system and promotion activity system;

[0016] 1.2) Data extraction:

[0017] Use ETL tools or write Python scripts to extract data from various data sources;

[0018] 1.3) Data cleaning, including:

[0019] Remove duplicates: Use SQL deduplication or Python’s Pandas library to deduplicate;

[0020] Correct incorrect data: handle null values ​​and outliers;

[0021] Convert data formats: ensure that the formats of all fields are consistent;

[0022] 1.4) Data Verification:

[0023] Perform quality checks on the cleaned data to ensure that the data is accurate.

[0024] Preferably, in step 2), specifically:

[0025] 2.1) Demand Analysis:

[0026] Determine the dimensions and indicators that need to be displayed in the report, including total sales, sales by region, and sales by product category;

[0027] 2.2) Data model design:

[0028] Design multidimensional data models and define dimensions and measures; dimensions include region and product category, and measures include sales and order quantity;

[0029] 2.3) Data Aggregation:

[0030] Aggregate data using SQL or Python to generate summary tables;

[0031] 2.4) Performance optimization:

[0032] Improve data query speed through index optimization and partition strategy techniques.

[0033] Preferably, in step 3), specifically:

[0034] 3.1) Choose the right database:

[0035] Choose the right data warehouse solution, including Amazon Redshift, Google BigQuery, and Snowflake;

[0036] 3.2) Data loading:

[0037] Import the preprocessed data into the data warehouse;

[0038] 3.3) Data Management:

[0039] Implement data lifecycle management strategies, including data archiving, backup, and recovery plans;

[0040] 3.4) Security Settings:

[0041] Configure access controls to ensure only authorized users can access sensitive data.

[0042] Preferably, in step 4), specifically:

[0043] 4.1) Interface design:

[0044] Develop RESTful API or Web service interface to receive query requests from the front end;

[0045] 4.2) Parameter analysis:

[0046] Analyze the query parameters sent by the front end and determine the query conditions;

[0047] 4.3) Query Construction:

[0048] Dynamically build SQL query statements based on parsed parameters;

[0049] 4.4) Execute the query:

[0050] Send the constructed query statement to the data warehouse for execution and return the query result;

[0051] 4.5) Results presentation:

[0052] Format the query results into JSON or XML format for front-end display.

[0053] The beneficial effects of the present invention are as follows:

[0054] The present invention achieves high efficiency of data processing and flexibility of query by combining preprocessing with real-time query. Compared with traditional methods, the present invention can respond to query requests faster, reduce data processing time, reduce the requirements for hardware resources, and support more extensive and complex query requirements. Experiments have shown that the present invention improves query efficiency by more than 30% when processing large amounts of data, significantly enhancing the system's usability and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a data report development method based on the combination of pre-processing and real-time query in an embodiment. DETAILED DESCRIPTION

[0056] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0057] Example

[0058] like Figure 1 As shown, this embodiment provides a data report development method based on the combination of pre-processing and real-time query, which includes the following steps:

[0059] 1) Data integration and purification: Use data scheduling technology to collect data from different sources, perform deep cleaning, and build a basic data warehouse.

[0060] Objective: Extract data from the order system and inventory system, clean it, and ensure data quality.

[0061] Specifically:

[0062] 1.1) Data source identification;

[0063] Data sources include:

[0064] Order system: including order ID, customer ID, order date, product ID, quantity, and unit price fields;

[0065] Inventory system: including product ID, product name, category, inventory, and supplier fields;

[0066] Other data sources: including customer management system and promotion activity system;

[0067] 1.2) Data extraction:

[0068] Use ETL tools (such as Apache NiFi, Talend, Informatica, etc.) or write Python scripts to extract data from various data sources;

[0069] 1.3) Data cleaning, including:

[0070] Remove duplicates: Use SQL deduplication or Python’s Pandas library to deduplicate;

[0071] Correct incorrect data: handle null values ​​and outliers;

[0072] Convert data formats: ensure that the formats of all fields are consistent;

[0073] 1.4) Data Verification:

[0074] Perform quality checks on the cleaned data to ensure that the data is accurate.

[0075] 2) Intelligent pre-processing and dimensional division: Based on business needs, basic data can be pre-processed into lists or statistical summaries of multiple dimensions.

[0076] Objective: Pre-calculate and store data for commonly used queries to improve query efficiency.

[0077] Specifically:

[0078] 2.1) Demand Analysis:

[0079] Determine the dimensions and indicators that need to be displayed in the report, including total sales, sales by region, and sales by product category;

[0080] 2.2) Data model design:

[0081] Design multidimensional data models and define dimensions and measures; dimensions include region and product category, and measures include sales and order quantity;

[0082] 2.3) Data Aggregation:

[0083] Aggregate data using SQL or Python to generate summary tables;

[0084] 2.4) Performance optimization:

[0085] Improve data query speed through index optimization and partition strategy techniques.

[0086] 3) Build a pre-processed data warehouse: Organize the pre-processed data into a form that is easy to query to form a pre-processed data warehouse.

[0087] Goal: Create an optimized data storage environment to quickly respond to queries.

[0088] Specifically:

[0089] 3.1) Choose the right database:

[0090] Choose the right data warehouse solution, including Amazon Redshift, Google BigQuery, and Snowflake;

[0091] 3.2) Data loading:

[0092] Import the preprocessed data into the data warehouse;

[0093] 3.3) Data Management:

[0094] Implement data lifecycle management strategies, including data archiving, backup, and recovery plans;

[0095] 3.4) Security Settings:

[0096] Configure access controls to ensure only authorized users can access sensitive data.

[0097] 4) Dynamic parameterized query assembly: The query request is received through the front-end user interface, and the back-end system dynamically assembles the query logic according to the request parameters to extract the required information from the pre-processed data warehouse.

[0098] Objective: Dynamically generate SQL queries based on front-end requests to implement flexible data reporting capabilities.

[0099] Specifically:

[0100] 4.1) Interface design:

[0101] Develop RESTful API or Web service interface to receive query requests from the front end;

[0102] 4.2) Parameter analysis:

[0103] Analyze the query parameters sent by the front end and determine the query conditions;

[0104] 4.3) Query Construction:

[0105] Dynamically build SQL query statements based on parsed parameters;

[0106] 4.4) Execute the query:

[0107] Send the constructed query statement to the data warehouse for execution and return the query result;

[0108] 4.5) Results presentation:

[0109] Format the query results into JSON or XML format for front-end display.

[0110] When this embodiment is implemented, query parameters are strictly verified to prevent SQL injection attacks; a cache mechanism is provided to directly return the results in the cache for repeated query requests to reduce the database load.

[0111] Through the above detailed implementation steps, this embodiment can efficiently generate sales reports, support multi-dimensional queries, and significantly improve the efficiency and flexibility of data processing.

[0112] The present invention and its embodiments are described schematically above, and the description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by it and designs a structural method and an embodiment similar to the technical solution without creativity without departing from the purpose of the invention, they shall all fall within the protection scope of the present invention.

Claims

1. A data report development method based on the combination of pre-processing and real-time query, characterized in that: The following steps are involved: 1) Data integration and purification: Use data scheduling technology to collect data from different sources, perform deep cleaning, and build a basic data warehouse; 2) Intelligent pre-processing and dimension division: pre-process basic data into lists or statistical summaries of multiple dimensions according to business needs; 3) Build a pre-processing data warehouse: organize the pre-processed data into a form that is easy to query, forming a pre-processing data warehouse; 4) Dynamic parameterized query assembly: The query request is received through the front-end user interface, and the back-end system dynamically assembles the query logic according to the request parameters to extract the required information from the pre-processed data warehouse.

2. A data report development method based on the combination of pre-processing and real-time query according to claim 1, characterized in that: In step 1), specifically: 1.1) Data source identification; Data sources include: Order system: including order ID, customer ID, order date, product ID, quantity, and unit price fields; Inventory system: including product ID, product name, category, inventory, and supplier fields; Other data sources: including customer management system and promotion activity system; 1.2) Data extraction: Use ETL tools or write Python scripts to extract data from various data sources; 1.3) Data cleaning, including: Remove duplicates: Use SQL deduplication or Python’s Pandas library to deduplicate; Correct incorrect data: handle null values ​​and abnormal values; Convert data formats: ensure that the formats of all fields are consistent; 1.4) Data Verification: Perform quality checks on the cleaned data to ensure that the data is accurate.

3. A data report development method based on the combination of pre-processing and real-time query according to claim 2, characterized in that: In step 2), specifically: 2.1) Demand Analysis: Determine the dimensions and indicators that need to be displayed in the report, including total sales, sales by region, and sales by product category; 2.2) Data model design: Design multidimensional data models and define dimensions and measures; dimensions include region and product category, and measures include sales and order quantity; 2.3) Data Aggregation: Aggregate data using SQL or Python to generate summary tables; 2.4) Performance optimization: Improve data query speed through index optimization and partition strategy techniques.

4. The data report development method based on the combination of pre-processing and real-time query according to claim 3 is characterized in that: In step 3), specifically: 3.1) Choose the right database: Choose the right data warehouse solution, including Amazon Redshift, Google BigQuery, and Snowflake; 3.2) Data loading: Import the preprocessed data into the data warehouse; 3.3) Data Management: Implement data lifecycle management strategies, including data archiving, backup, and recovery plans; 3.4) Security Settings: Configure access controls to ensure only authorized users can access sensitive data.

5. The data report development method based on the combination of pre-processing and real-time query according to claim 4 is characterized in that: In step 4), specifically: 4.1) Interface design: Develop RESTful API or Web service interface to receive query requests from the front end; 4.2) Parameter analysis: Analyze the query parameters sent by the front end and determine the query conditions; 4.3) Query Construction: Dynamically build SQL query statements based on parsed parameters; 4.4) Execute the query: Send the constructed query statement to the data warehouse for execution and return the query result; 4.5) Results presentation: Format the query results into JSON or XML format for front-end display.

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