ETL is a procedure that plucks data from source systems, transforms the information into a consistent data type, and then stores it into a single depository. ETL testing leads to the process of validating very fine and qualified data by preventing duplicate records and data loss.
It also ensures that data transfer from various sources to the central data warehouse transpires with strict adherence to transformation rules and agrees with all the validity checks. It varies from data reconciliation used in database testing in that ETL testing is applied in data warehouse systems and used for obtaining relevant information for analytics and business intelligence.
Eight stages of the ETL testing process
Efficient ETL testing identifies problems with the source data early on before it is loaded to the data repository, as well as discrepancies or uncertainties in business rules designed for guiding data transformation and integration. We can also break down the process into eight stages.
Identifying business requirements: designing the data model, defining business law and assessing reporting needs dependent upon client expectations. It’s essential for the starting year for the project scope is clearly defined, documented and understood fully by testers.
Validating data sources: performing a data account check and verify that the table and column data type meets the specifications of the data model. You should make sure to check keys are in place and remove any duplicate data, but if not performed correctly, the aggregate report would be mistaken or misleading.
Design test cases: designing ETL mapping scenarios, creating SQL scripts and defining transformational rules becomes essential for validating the mapping document and ensuring that it contains all of the information.
Extracting data from source systems: executive ETL test as per the business requirements by identifying bugs or defects encountered during testing and making a report. It is essential for detecting and reproducing any defects, reports, fixing the bugs, resolving them and closing bug reports.
Applying transformation logic: ensuring that data is transformed to match the schema of the target data warehouse. You should check the data threshold, alignment and validate its floor. It will ensure the data type matches in the mapping document for each column and table.
Load data into target warehouse: you can also perform a record count check before and after the data is migrated from staging to the data warehouse.
Summary report: define layout, options, filters and exporting functionality of summary report. This report makes decision-makers or stakeholders know about the details and results of the testing process.
Test closure: file test closure. The final step for the ETL tester is to test the tool, its functions and the ETL system.
Nine types of ETL tests
The ETL process testing suits four category groups: new (source data), migratory (source data migrated to the data warehouse), update tests(new information applied to the stored data), and report testing. ETL testing covers four main categories (validate data, make calculations).
ETL Tests that may be executed in each stage are:
- Production validation or table validation is also known as “production reconciliation”, validates and matches data in production systems with source data. It protects information from defective logic, broken loads, or non-system loaded operating procedures.
- The reference count test source confirms that the number of documents loaded into the target database corresponds to the estimated record number.
- Source for the target data checking guarantees that estimated figures are applied without loss or truncation to the target structure and that data values follow transformation requirements.
- Metadata checking is carried out through ETL framework metadata data sort, length, index and limit tests (load statistics, reconciliation totals, data quality metrics).
- Performance testing ensures that data can be loaded in the data store in expected time frames. The reaction from the test server is suitable for performance and scalability for several users and transactions.
- Data processing tests perform SQL queries for each row to ensure the data is converted properly according to market rules.
- To ensure that the ETL framework cannot reject accepted default values or reports on invalid data, data quality testing runs syntax tests and referenced tests.
- Test of data inclusion ensures the proper loading and control of the threshold values by data from all suppliers to the destination data warehouse.
- The test report data in the summary report, configuration and functionality verification, and measurement are carried out as intended.
To ensure that the ETL architecture works well on other systems, testing can also require user acceptance testing, GUI testing and application migration testing during the ETL phase. Incremental ETL checks can validate the processing of new logs and modifications.
Challenges in ETL process testing
Identifying challenges at early stages in the ETL testing procedure to prevent bottlenecks and costly setbacks. Building a source to target mapping documents and establishing clear business requirements from the beginning is essential.
Frequent changes to requirements requiring critical testers to change the logic in scripts could significantly slow progress. ETL testers are required to estimate the data transformation requirements, the time required for its completion, and a clear understanding of the end-user requirements.
A few other challenges to be washed out from the beginning include:
- Data lost or compromised during the migration process.
- Limited source data accessibility.
- Data migration requirements are underestimated.
- Incomplete or duplicate results.
- A vast quantity of historical data makes it impossible to assess ETL in the target system.
- The climate of the unstable testing.
- Utilizing outdated ETL tools.
How to find the best ETL testing tool?
ETL testing tools are used for enhancing its productivity and simplify the process of regaining information from big data to gain insights. The tool itself comprises ideas and rules for obtaining and processing data, eliminating the requirement for traditional programming methods that are expensive and labour intensive.
Another benefit is that ETL process testing tools have built-in adaptability with cloud data warehouse, ERP and CRM platforms such as Amazon Web services, Salesforce, Oracle, kinases, Google cloud platform and many more. abilities to look for while comparing ETL testing tools include:
- Graphic interface for simplifying the design and development.
- Automatic code generation for speeding development and reducing errors.
- Built-in data connectors that can access data stored in file format, a database, package application on the legacy system.
- Content management facilities that facilitate context switching for ETL development, testing and production environments.
- Sophisticated debugging tools will allow you to track data flow in real-time and reports on a row by row behaviour.
Cloud-native ETL tools meant specifically for cloud computing architecture enables a business to read the full benefits of a data warehouse endeavour.