A Look at ETL Testing: Importance, Process and Type

ETL stands for Extract, Transform and Load. It's a fundamental approach used by data integration tools and BI platforms to convert raw data into valuable insights.

Here's how it works:

1. Extraction: Gather historical or real-time data from multiple systems (ERP, CRM, third-party sources) in various formats.

2. Transformation: Place data in a staging area and reformat it to a standard model (e.g., $34.5, 0.9 cents, and $01,65 convert to $34.5, $0.9, $1.65).

3. Load: The final stage of an ETL process is loading the structured and formatted data into a database.

What is ETL testing and why do we need it?

ETL testing ensures data integrity during the transfer from source to Data Warehouse. It mitigates risks such as:

• Multiple sources with varying formats.
• Large and growing data volumes.
• Error-prone data mapping processes, leading to duplicates and quality issues.

Common ETL Testing Errors:

• Invalid source values: Resulting in missing data at the destination.
• Dirty data: Not conforming to mapping rules.
• Inconsistent formats: Between source and target databases.
• Input/output bugs: Accepting invalid values, rejecting valid ones.
• Performance issues: When handling multiple users or large data volumes.

Now, how do we ensure that data was safely mapped, transformed and delivered to its destination.

The ETL Testing process:

Understanding Businees Requirements:

Designing an effective ETL testing process requires understanding your organization's business requirements. This involves examining its data models, business workflows, reports, sources and destinations, and data pipelines.

Data Source Indentification and Validation:

Identify source data and perform initial schema checks and table validation to ensure the ETL process aligns with business requirements.

Creating and Executing Test Cases:

Source-to-target mapping and test case design are the the next steps:

• Check that all expected data is loaded into target database.
• Compare the number of records between source and target tables.
• Check if there are any rejected tables.
• Check that the data is displayed in full in target database.
• Check boundary value analysis.
• Compare unique values of key fields between source and target tables.
• Write ETL test cases in SQL with queries for both source and target data extraction.

Data Extraction and Reporting:

Base on business requirements and use cases. During test case execution, identify the different kinds of errors or defects, try to reproduce them, and log them with adequate details and screenshots.

Applying Transformations:

Ensure that transformations match the destination data warehouse schema, validate dataflow, check data thresholds and confirm data types align with mapping documents.

Loading Data into The Data Warehouse:

Perform record count checks before and after data movement, verify rejection of invalid data, and acceptance of default values.

Re-Testing the Bug(Regression Tesing)

Retest fixed bugs in the staging environment to ensure no traces remain and confirm no new defects have been introduced.

Summary Report and Test Closure

Prepare a detailed summary report of the testing process, defects, and test cases. Test the report's options, filter, layout, and export functionality. Inform stakeholders of any incomplete steps.

Types of testing

Production Validation and Reconciliation: validates the order and logic of the data in production.

Source-to-target Validation: Ensure data count matches between source and destination.

Metadata Testing: check the data types, indexes, lengths, constraints, schemas, and values between the source and target systems.

Completeness Testing: This verifies that all source data is loaded into the destination system without duplication, repetition or loss.

Transformation Testing: Confirm consistent data transformations.

Accuracy Testing: Ensure data content remains unchanged despite format/schema changes.

Data Quality Testing: this testing type focuses on data quality to identify invalid characters, precisions, nulls, and patterns. It reports any invalid data.

Report testing: Check the data in the summary report, determines if the layout and functionality are appropriate, and performs calculations for additional analytical requirements.

Application Migration Testing: Application migration testing verifies whether the ETL application is functioning properly following migration to a new platform or box.

Data and Constraint Checks: This testing technique checks the datatype, length, index and constrains.

Conclusion:

• ETL testing is crucial for ensuring data integrity and quality during the ETL process. It addresses risks like data loss, corruption, and inconsistency from multiple sources, large volumes, and complex mappings.
• By understanding business requirements, validating data sources, and executing detailed test cases, organizations ensure accurate data extraction, transformation, and loading.
• Effective ETL testing results in reliable data warehouses that support accurate business analytics and decision-making.

Reference:

[1]. ETL Testing: Importance, Process, and ETL Testing Tools.

[2]. ETL Testing: Processes, Types, and Best Practices

[3]. What is ETL Developer: Role Description, Process Breakdown, Responsibilities, and Skills