Complete Guide of ETL Testing
ETL testing is a critical step for enterprises to maintain data quality and insights.
This article covers everything you need to know about ETL testing tools and his processes.
ETL is the abbreviation of Extract-Transform-Load. These three operations are performed on data. To understand ETL, we need to understand the process of data loading from source to data warehouse. The extraction process occurs on an OLTP database, and then the transformation is done to match the schema of the data warehouse. This is what is meant by ETL. More recently, text files, legacy system files, and spreadsheets can also be handled using ETL.
To maintain any e-commerce or digital business, the best way is to store the data in data warehouses which can combine data from various sources and can maintain them in a uniform and compatible structure using ETL, as ETL can make dissimilar data into similar data, this is what is called transformation. ETL has become a global hit especially after the dependence of enterprises to use business intelligence tools to make reports, analyses, and insights.
Understanding ETL Processes
Step 1- Extraction: The relevant data is extracted from the source system.
Step 2- Transformation: The major chunk of effort is spent on making keys. There are various types of keys such as primary, foreign, alternate, composite, and surrogate keys. All of these are made to link all the tables and transform the data for further ETL testing. Then the omission of anomalies takes place. This is called data cleaning. It involves the eradication of the following issues; incompatible data conflicts and data errors.
Metadata is also created at this stage to make the process more diagnosis friendly. This makes sure data quality is maintained.
Step 3- Loading: The data is loaded in the directory called data warehouse where it will be used for reporting and insights. Aggregates are built and functions such as summarizing, sorting, and ordering takes place. This enhances the performance of end-user queries.
Step 4- Using the data: All the previous steps make sense when this transformed data is used to perform processes and is browsed for further usage.
Understanding ETL Testing
The ETL steps need to be verified and tested for accuracy. The loaded data is checked to see if it has been correctly moved and all business transformations are correct. Data is also verified from multiple angles. ETL testing is done in the following order:
Identification of data sources.
Identification of data requirements.
Acquisition of data.
Implementation of business logic.
Implementation of dimensional modeling.
Population of data.
Generation of reports.
ETL testing ensures business intelligence efforts are going in the right direction. All integration points are double-checked, validated, and verified. ETL testing tools make sure there are no issues in the duplication removal, truncation processes, migration processes, standard procedures, and architecture details. From source data to staging tables to target warehouse all entry points are checked and it is made sure that the BI tool is using the correct data with maximized query performance.
Understanding the difference between ETL and ELT
When the step of loading and transforming are swapped, ETL becomes ELT. ETL moves data from source to staging tables but ELT takes advantage of data warehouse to do basic level transformations. The staging steps are bypassed. ELT works well with data leakages. One transforms the data before and one transforms the data after loading it into the repository.
Understanding ETL Testing Processes
Effective ETL testing has eight steps that aim to detect problems at earlier stages, removal of inconsistencies, duplicates, ambiguities, and faulty business rules before data integration. Let’s look at all eight stages in detail.
- Identification of business requirements: This defines the scope of the project clearly with proper documentation and complete designing of the data model, complete definition of the business flows and all reporting needs and expectations are figured out.
- Validation of data sources: This defines the data checks and verifications that are to be performed on the table data according to the data model. Keys are checked and duplicate data is removed. In case this stage fails, the aggregated report will be a disaster.
- Designing of test cases: This defines the ETL mappings, SQL scripts, and rules of transformation. Mapping is also validated to make sure it contains the complete information.
- Extraction of data from source systems: This defines ETL tests that need to be performed according to the business reporting requirements. Errors and defects are reported back for correction.
- Application of transformational logic: This defines the matching of schema and data warehouse. The data flow, alignment, and thresholds are checked to ensure there is no data type mismatch.
- Loading of data in the targeted data warehouse: This defines the performance of count checks when data moves from source to staging to data warehouse. This confirms that invalid data gets rejected and only correct forms of data are processed.
- Production of summary report: This defines the filters, layout, options, and exporting features of the final portal with all the numbers and reports.
- Closing of procedure: This defines the exit scenario after all the ETL testing is done. All ends are matched and tested.
Understanding the importance of ETL Testing
ETL testing can be applied to multiple tools and databases especially in the DQM and Data Governance industry. ETL testing makes sure that loaded data moves correctly and efficiently from source to destination and all business transformations are correct. There are two documents that help data engineers perform these; ETL mapping sheet, and Database Schemas. The former contains the complete information with look-up reference tables. The later keeps the validation of the mapping sheets.
An ETL engineer and tester uses mapping tables frequently to make sure SQL queries work well.
Understanding ETL Testing Types
There are many angles from which ETL testing can be performed. As technologies expand, there are more loose ends that need to be tested. For each different end, there needs to be a specialized test. Let’s look into the 15 most useful ETL test types.
BiG EVAL and ETL Testing
BiG EVAL takes away the manual effort from testing your ETL or ELT processes and your data warehouse. This is done by comparing and checking test data between the source systems, the staging area, the data warehouse, and all the components in between.
Exactly how you would do it manually – but we do it automated. Using BiG EVAL you automate the test processes for your data warehouse system and the ETL or ELT processes.