ELT Testing Automation: An Overview

ETL Extract/Transform/Load is a process where data from source systems are extracted, transformed into a consistent data type, and then loaded into a centralized database. The process of evaluating, confirming, and certifying data while avoiding duplicate records and data loss is known as ETL testing.

ETL testing makes sure that all validity checks are met and that all transformation rules are strictly followed while transferring data from diverse sources to the central data warehouse. ETL testing is different from data reconciliation used in database testing since it is intended to extract pertinent data from data warehouse systems for analytics and business intelligence. Additionally, handling data integrations, sorts, and joins; all fall under the various advantages of the ETL process, as do data migration for recent systems.

The difficulty over here is that, despite the rising use of ETL in data management systems, comprehensive ETL testing reflects how lengthy and manual the entire testing process is. It demonstrates how an unacceptably significant number of defects are introduced into the production environment in the absence of sufficient quality checks.

The 8 Stages of ETL Testing

Determine the business requirement: Based on customer expectations, create the data model, specify the business process and evaluate the reporting requirements. We start from this stage so that the testers have a clear understanding of the project’s scope and its documentation.

Verify the data source: Verify the table and column data types comply with the data model’s requirements by performing a data count check. Eliminate duplicate data and ensure that the check keys are in order. The aggregate report may be erroneous or inaccurate if it is not prepared properly. 

Design test cases: Develop SQL scripts, specify transformational rules, and design ETL mapping scenarios. The mapping document needs to be validated as well in order to make sure it is accurate and contains all the information.

Extract data from source systems: Perform ETL tests in accordance with business requirements. Report on the various kinds of bugs or errors found during testing. Before moving on to Step 5, it is crucial to identify and recreate any defects, report, repair the bug, resolve, and close the bug report. 

Employ transformation logic: Make sure the data are transformed to fit the target data warehouse’s schema. Check the data threshold, alignment, and data flow. By doing this, each column and table’s data type is ensured to match the mapping document. 

Load data into the target warehouse: In this stage, the extracted and transformed data are loaded into the target database. Before loading the data, the database must be indexed, and the constraints must be disabled in order to provide an efficient data load.

Brief report: This stage verifies the layout, options, filters, and export functionality of the summary report. This report informs decision-makers and other stakeholders of the testing process’s specifics and outcomes.

Test closure: File a test closure. This final step is for the ETL tester to test the tool, its functions, and the ETL system.

Challenges to Expect

The volume of data: ETL operations typically handle millions of transactions and data records in terms of data volume. Writing SQL queries to validate the data and manually analyzing the results in Excel spreadsheets is difficult due to the volume of data. 

Several data owners: Data may come from a variety of source systems that use various technologies that are governed by various data governance policies, and most crucially, have various masters. To the established business needs of the Data Warehouse, it is not merely necessary to detect non-conformances, but it is equally crucial to locate the defect’s root cause. If that data condition cannot be resolved, it becomes a completely another issue, and you will need to continue managing data exceptions.

Availability of the test bed: This problem is mostly a result of the two issues mentioned above. Most businesses struggle with the difficulty of developing a test bed that has a representative sample of data from all the data sources. The probability of bugs making their way into production greatly increases when the proper sample set isn’t used for testing. 

Dynamic Data Governance Policies: The data providers or the source systems may be replaced over time. As a result, it is crucial that testing procedures be flexible enough to handle significant changes.

Estimating Testing Effort: The organization frequently experiences difficulties as a result of its ignorance of the source data’s nature. Therefore, creating an exhaustive business requirement/data rules document is an expensive process. As previously said, the testing effort, in this case, goes beyond verifying that the ETL process created is in accordance with the business standards. The majority of the build effort is spent on identifying data errors and fixing the ETL processes to correct them.

Why automate ELT testing?

Automation guarantees that the ETL procedures are not only tracked but also recorded with up-to-date metadata on every data extraction, transformation, movement, and manipulation as it approaches the ultimate analytical asset (a report, an analytic result, a visualization, a dashboard widget, and so on). These metadata are an essential component of the automation software and are always up to date, so it is not an afterthought. Both the business community and the technical implementation team can benefit from it.

A good degree of data quality is equally dependent on test automation. The more we test, the more bugs will be found and fixed before going live. This is very important for business intelligence projects.

ETL testing is typically done manually, making it a time-consuming and error-prone process. In addition to supporting automated testing on older code after each new database build, automating ETL tests enables regular smoke and regression testing with little to no user input. Not only can automation aid in test execution, but it can also help with test design and management.

The choice to exercise automated tools for ETL testing depends on the availability of funds to cover the costs associated with meeting complex testing criteria. It’s crucial to keep in mind that internal test tools are preferable to no test automation at all. Test automation will ultimately reduce a lot of time. 

Summing Up

Any business aiming for Continuous Delivery of its software systems must use ETL test automation. ETL validation however requires a significant amount of manual work, including writing ghost code, locating the necessary data, and contrasting desired outputs. All of these processes can be efficiently automated with the help of model-based testing and intelligent test data management, which also enables other teams to utilize the same data sources in parallel development environments.

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Written by Infiwave Solutions