ETL Testing involves validating data during the Extract, Transform, and Load process to ensure accuracy, data integrity, and correctness when it moves from source systems to target databases.
The key stages include: Data extraction validation, Data transformation validation, Data loading validation, and ensuring data integrity, performance, and quality.
A Data Warehouse is a central repository that stores integrated data from multiple sources for reporting, analysis, and business intelligence.
Data Completeness Testing,
Data Accuracy Testing,
Data Transformation Testing,
Data Integrity Testing,
Performance Testing,
Regression Testing.
By checking if all records are accurately extracted from the source system, matching the number of records, and verifying data formats.
ETL Testing validates the data during the extract, transform, and load processes, whereas Database Testing focuses on validating the integrity and consistency of data stored in a database after it's loaded.
It involves validating that data transformations are applied as per the business rules, ensuring that data is correctly transformed from source to target systems.
Handling large volumes of data,
Dealing with complex transformation logic,
Identifying data discrepancies,
Ensuring data quality and performance.
Data Mapping defines how data from the source is transformed and loaded into the target system. It includes mapping fields from source to target with the required transformations.
By validating that data is consistent, accurate, and complete between source and target systems. This includes checking relationships, constraints, and duplicate records.
Incremental Load Testing verifies that only new or updated data is extracted, transformed, and loaded into the target system without reloading the entire data set.
Data Cleansing ensures that incorrect, incomplete, or irrelevant data is identified and corrected before loading it into the target system.
Measuring data load times,
Monitoring system throughput,
Identifying and resolving bottlenecks,
Optimizing the ETL process to handle large data volumes efficiently.
Full Load: All data is extracted and loaded into the target system,
Delta Load: Only the data that has changed (new or updated) is extracted and loaded.
SQL is essential in ETL Testing for writing queries to validate the extracted, transformed, and loaded data. It helps in comparing data between source and target systems.
By writing test cases to identify duplicate records during extraction or loading and ensuring that unique constraints and deduplication processes are in place.
Common ETL Testing tools include QuerySurge, Informatica, Talend, Apache Nifi, DataStage, and manual SQL queries for validation.
By re-executing previous test cases to ensure that recent changes in ETL processes haven’t broken existing functionality or introduced new defects.
Metadata refers to data about data. It provides information about the data sources, transformations, and data structures used in the ETL process.
Data Profiling is the process of analyzing the source data to understand its structure, quality, and content before it is used in the ETL process.
