ELT testing

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Basic and intermediate ELT testing principles are covered in the ELT testing lesson. For both novices and experts, we have an ELT Testing tutorial.

After applying joining fields, performing computations, deleting inaccurate data fields, and other transformations, ETL tools take the data out of all the various data sources and load it into a data warehouse.

ETL testing is carried out to guarantee that, following business transformation, accurate data has been loaded from a source to a destination. Additionally, it entails confirming data at various points in time that are utilized to connect the source and the destination.

ETL (Extraction, Transformation and Loading) Testing

Prior to data being sent to live data warehouse systems, ETL testing is completed. It is also known as product reconciliation or table balancing. With regard to its scope and the procedures used, ETL testing differs from database testing.

Process for ETL testing

Like other testing process, ETL testing also go through some testing processes.

ETL testing performed in five stages.

1. ETL testing identifies data sources and requirements.
2. Data recovery
3. Implement dimensional modeling and business logic.
4. Build and populate data
5. Build reports

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Types of ETL testing

The types of ETL testing are:

1. Testing the New Data Warehouse: It is constructed and validated from the ground up. Several data sources and the customer’s requirements are the sources of input for this testing. ETL tools, however, are used in the construction and verification of the new data warehouse.

• Business Analyst: Business Analyst gathers and documents the requirements.
• Infrastructure People: These people set up the test environment.
• QA Testers: QA Testers develop test plans and test scripts and then execute these test plan and scripts.
• Developers: Developers perform the unit test for each module.
• Database Administrator: Database Administrator test for the performance and also for the stress.
• Users: Users do functional testing, which includes UAT (User Acceptance Testing).

2. Production Validation Testing: When data is sent to production systems, this testing is carried out. To make sure that the data do not jeopardize production systems, the Informatica Data Validation option automates ETL testing and management functions.

3. Source to Target Validation Testing: This kind of testing is carried out to confirm that the data values have been translated into the anticipated data values.

4. Application Upgrade: The test development time is reduced because this kind of ETL testing is automatically generated. This kind of testing verifies that the data that is retrieved from an older application and the data in a new application are exactly the same.

5. Metadata Testing: This sort of testing involves measuring the length, type, and check index/constraint of the data.

6. Testing for Data Accuracy: This step verifies that the data is correctly loaded and transformed in accordance with expectations.

7. Data Transformation Testing: In various situations, data transformation testing is carried out. Writing a single source SQL query and comparing the result with the target will not accomplish the goal. For every row, several SQL queries must be executed in order to confirm the transformation rules.

8. Data Quality Testing: This type of testing includes reference and syntax checks. Data quality is carried out during business processes to prevent errors caused by dates or order numbers. Tests of syntax: If a character is invalid, if a character pattern occurs, if the case is ordered incorrectly, etc., it will report dirty data. Tests of Reference: The data will be verified based on the data model.

As an illustration, the quality testing of Customer ID data comprises many checks, such as date, precision, and number checks.

9. Incremental ETL Testing: When new data is added, this testing is done to ensure that the old and new data are both intact. After data is inserted and updated during an incremental ETL process, incremental testing confirms that the system still functions properly.

10. GUI/Navigation Testing: This testing is carried out to examine the front-end reports’ GUI or navigational elements.

11. Migration Testing: ETL is handling the testing, and the customer has an existing data warehouse. However, clients are searching for instruments to boost productivity. These actions are part of it:etl testing training in vizag

Test design and validation
preparing the testing environment
carrying out the validation test
Notifying others about the bugs
12. Modification Requests: data added to an already-existing data warehouse in this instance. Customers may need to implement new rules or modify the current business rules in certain situations.

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Tasks performed in ETL Testing

Tasks involved in ETL testing are:

• Understanding of data, used for reporting
• Data Model Reviewing
• Mapping of the source to target
• Checks the data in the source data
• Validation of packages and schema
• In the target system, data verification should be done
• Verification of aggregation rules and data transformation calculation
• Data comparison between the target system and data source
• For the target system, quality and data integrity should be examined.
• Performance testing of data.

Distinctions between Database Testing and ETL
Data validation is a part of both ETL and database testing, however they are not the same. Database testing is done on transactional systems, while ETL testing is typically done on data in a data warehouse. The transactional database receives data from several applications.

Tasks carried out during ETL Testing
The following procedures are included in ETL testing:

verification of data transfer from the originating system to the destination one.
Verification of data counts in the source and destination systems.
ETL testing confirms that the extraction and transformation meet expectations and requirements.
ETL testing checks to see if the keys and table relations join are preserved throughout the transformation.
The task carried out during database testing
Valid values, accuracy, and correctness of the data are the main concerns of database testing.

Database testing carries out

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Testing ETL in data transformation
Because the data transformation cannot be completed by performing a single SQL query and comparing the output with the target, it is rather hard to do. We must create many SQL queries for each row in order to check the transformation rules during the ETL testing phase of data transformation.etl testing training in vizag

We must select enough sample data from the source system to apply the transformation rule in order to carry out effective ETL testing for data transformation.

The following are important steps in conducting ETL testing for data transformation:

1. Creating a scenario for the incoming data and the anticipated outcomes is the first stage. We will now work with the business customer to validate the ETL testing. The best testing is ETL testing.

Step 2: The test data must be created in accordance with the scenario. When the scenario spreadsheet is updated, the ETL developer will automate the entire process of populating the datasets, allowing for flexibility and mobility.

Step 3: Make use of data profiling. The output will allow you to compare the numbers submitted in each field with the range of values in the source and target data.etl testing training in vizag

Step 4: We’ll confirm that the fields produced by ETL were processed accurately. Take surrogate keys, for instance.

Step 5: Verifying that the data types in the warehouse match those listed in the data model or design will be done.

Step 6: Data scenarios that test the referential integrity between tables will be developed.