ETL testing challenges and solutions
ETL testing is a complex process that involves several challenges. Here are some of the significant challenges faced by testers during the process:
Challenge: Bad data sources
Manual data entry can introduce errors, both intentional and unintentional, and inconsistencies can arise when users input units of measurement in written or abbreviated forms. Additionally, manual data entry can lead to duplicate records, particularly when multiple people are involved in the same project.
Solution: To overcome the challenge posed by bad data sources during ETL testing, several strategies can be employed:
- Implement tools (Macro) for historical data: In cases where historical data needs to be transferred to standardised formats, the development team can create macro tools. These tools automate the transformation process, ensuring data consistency and accuracy. By standardising the historical data, it becomes easier to validate and integrate it into the ETL process
- Create new templates/tools for upcoming data: For new data entries, it is beneficial to develop new templates or tools to assist business users. These tools can provide data validation rules, drop-down menus, or other features that enhance data entry accuracy. Additionally, improving the application used for data processing can ensure that clean and up-to-date data is available for all future monthly data uploads, reducing the chances of human errors during manual data processing.
By implementing these strategies, organisations can mitigate the challenges caused by bad data sources, improve data quality, and streamline the ETL testing process.
Challenge: Huge and complicated data sets
The presence of a vast amount of data in various sources, including multiple file formats, compatible databases and proprietary data formats, highlights the need to harmonise and consolidate the data into a single source. This consolidated source then feeds into BI reports, which drive business decision-making. Cleansing and harmonising the data is necessary to ensure its accuracy, consistency and reliability for effective analysis.
Solution: To address the challenge of dealing with huge data during ETL testing, the following approaches can be beneficial:
- Utilise sampling techniques: Working with large volumes of data can be time-consuming and resource intensive. To optimise coverage, testers can employ sampling techniques. By selecting representative subsets of data, they can focus their testing efforts on a smaller yet representative portion of the overall dataset. This helps in identifying potential issues and validating the ETL process without the need to process the entire dataset.
- Automate data comparisons: Comparing large datasets manually can be error-prone and time-consuming. To overcome this challenge, testers can leverage automated data comparison tools such as Beyond Compare or Datatific tool. These tools provide efficient and accurate comparisons between source and target data, highlighting any discrepancies or inconsistencies. Automating the comparison process helps save time and ensures thorough validation of the transformed data.
By employing sampling techniques and leveraging automated data comparison tools, we can effectively tackle the challenges associated with handling large volumes of data in ETL testing. These approaches contribute to improved efficiency, enhanced coverage and more accurate validation of the ETL process.
To overcome the challenges posed by complicated data during ETL testing, the following strategies can be implemented:
- Rank data sources: When dealing with complex data, it can be helpful to rank data sources based on their importance. Identify the critical data sources that significantly impact the business processes or decision-making. Prioritise testing efforts on these high-priority data sources to ensure their accuracy and reliability
- Collaborate with business users (BU): Close collaboration with BUs during ETL testing helps them to understand the business requirements and identify any gaps or inconsistencies. By addressing these gaps early on, we can align the ETL process with the business expectations, reducing the risk of errors and ensuring accurate data transformation
- Involve client testing early: Encourage clients to involve the testing team as early as possible in the ETL process. This allows us to provide feedback and identify potential issues during the design and development stages. Early involvement ensures that complications related to complex data are identified and addressed promptly, reducing rework and saving time and effort
- Obtain test cases/scenarios for UAT: Request the client provides test cases or scenarios for user acceptance testing (UAT) stage. These test cases align the ETL process with the client’s specific requirements and expectations. Having predefined test cases facilitates smooth UAT execution, streamlines the validation process and reduces the overall time and effort required for testing.
By implementing these strategies, we can effectively tackle the challenges associated with complex data in ETL testing. Close collaboration with the business users, early involvement in the process and leveraging client test cases contribute to ensuring accurate data transformation and meeting the business requirements.
