Extract Transform and Load (ETL) smoke test method

Abstract

An Extract Transform and Load (ETL) smoke test method includes that call instructions are obtained through a database connection program and used for calling a target test table; the database connection program calls the ETL smoke test storage process according to the obtained call instructions; the ETL smoke test storage process achieves a smoke test for the target test table and returns test results to the database connection program; and the database connection program makes up the test results into an ETL smoke test table and outputs the ETL smoke test table. The invention further provides a corresponding ETL smoke test device. According to the ETL smoke test method and the ETL smoke test device, operation which needs to be repeatedly done by ETL test staff in different projects is extracted to be automatically completed in bulk, test time of the ETL test staff is saved, low-grade mistakes made by the test staff in boring repeated operation are avoided, mastering for overall quality of a project can be achieved quickly, and smooth operation of the integral project is guaranteed.

Description

technical field [0001] The present application relates to ETL (ie, data extraction, transformation, and loading) testing methods, and more particularly to ETL testing methods in data warehouse applications. Background technique [0002] Databases have been widely used in the field of information technology. In almost every department of our social life, there are various databases that store various data that are closely related to our lives. [0003] As a branch of the database, the data warehouse is usually a subject-oriented, integrated, time-changing, but relatively stable data collection, which is used to support the management decision-making process. The information in the data warehouse is not simply extracted from various business systems, but goes through a series of processes of processing, sorting and summarizing. [0004] ETL is responsible for extracting data from distributed and heterogeneous data sources, such as relational data and flat data files, to a tem...

AI Technical Summary

Problems solved by technology

[0008] 1. A lot of manual operations are still required, and errors may occur in the process of replacing SQL statements

[0009] 2. Executing a SQL statement for each indicator will repeatedly read the database table, not only the efficiency of test execution is low, but also the pressure on the database and server is also great

[0010] 3. The output is all the execution results of SQL statements, which is difficult to analyze and not intuitive for non-ETL testers

Although a lot of manual operations are avoided, each test case in the stored procedure is independent, and the entire optimization is not performed. In this way, every time a test case is executed, a full table scan of the table under test is required, resulting in a large amount of unnecessary I. / O (input / output), poor execution performance, high pressure on database and server

Images