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  • 28-Jul-2021 04:54 pm
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ETL (Extract/Transform/Load) is the process of extracting data from a source system, transforming the information into a single data type and then loading the data into a single repository.  ETL testing is the process of verifying, validating and qualifying data.  ETL is the process of avoiding duplication of records and loss of data.

ETL testing ensures that the transfer of data from heterogeneous sources to a central data warehouse is performed in strict compliance with transformation rules and meets all validation checks ETL testing is applied to data warehouses to generate relevant information for analysis and business intelligence. ETL testing is applied to a data warehouse to generate relevant information for analysis and business intelligence and is therefore different from data matching used in database testing.

Career in ETL

It is better to pursue an ETL certification as it will help the people to learn the tool easily and they will be able to perform any kind of test. You will also be given ETL interview questions which you can practice to get ETL jobs easily. There is a huge demand of ETL certified professionals in the job market.

Steps in the ETL testing process

Effective ETL testing identifies problems with source data before it is loaded into the data warehouse, and inconsistencies and ambiguities in the business rules governing data transformation and integration. This process is divided into eight steps.

Understanding business requirements

We design the data model, define the business flows and assess the reporting needs against your expectations. It is important to start here to ensure that the scope of the project is clearly defined, documented and fully understood by the testers.

Validation of data sources

Perform data count validation to ensure that the data series of tables and columns meet the data model specifications. Check that keys are aligned and remove any duplicate data. Failure to do this correctly may result in inaccurate or misleading overall results.

Designing test cases

Design ETL mapping scenarios, create SQL scripts and define transformation rules. It is also important to check that all the information contained in the mapping document is captured.

Data extraction from source systems

Data is tested according to business requirements. Errors are identified and deficiencies are detected during the testing and then a report is generated. It is important to identify and reproduce errors, report them, correct and resolve them and close the error message.

Implementation of conversion logic

Ensure that the data is converted to the target data warehouse schema. Check data delimitation, alignment and validation of the data stream. This will ensure that the data rows in each column and table correspond to the mapping document.

Load the data into the target layer.

Check the number of records before and after the data is transferred from the preparation phase to the data warehouse. Ensure that invalid data are rejected and default values are accepted.

Summary report

Review the summary report layout, options, filters and export options. This report allows decision makers to see the details and results of the testing process and, if any steps were missed then the reason behind should be found.

Test conclusions

Completion of file testing

Types of ETL testing

ETL testing can be broadly divided into four types: new system testing (data from different sources), migration testing (data moved from the source system to the data warehouse), change testing (new data added to the data warehouse) and reporting testing (data validation and calculation). Let us discuss other types of tests.

Output validation

Output validation, also known as 'output reconciliation' or 'table balancing', checks the data in the production system and compares it with the source data. It helps to protect the data from faulty logic, failed loads and workflows that fail to load the system.

Source to destination count test

The source-to-target count test verifies that the number of records loaded into the target database matches the expected number of records.

Source-to-source testing of target data

Testing the source and target data ensures that the intended data is transferred to the target system without loss or truncation and that the data values after conversion meet expectations.

Metadata testing

Metadata testing checks the type, length, index, and constraints of the ETL application metadata.

Performance testing

Performance testing must ensure that data is loaded into the data warehouse in a timely manner and that the test server is performing and scalable enough for a large number of users and operations.

Data conversion testing

Data conversion testing involves running SQL queries on each row to verify that the data is converted correctly according to business rules.

Data quality testing

Data quality testing should include syntax tests and reference tests to ensure that the ETL program rejects, accepts default values, and reports erroneous data.

Data integration testing

The data integration test confirms that data from all sources have been correctly loaded into the target dataset and checks threshold values.

Test report

The report test analyzes the summary data from the report, verifies that the design and functions meet expectations, and performs calculations.

Overview of ETL testing tools

The ETL tools are used to test the ETL process, i.e., the process of extracting, transforming, and loading data from a data warehouse. Usually for manual testing of the ETL process the SQL query testing method is chosen which is a tedious, time consuming and error prone process. Therefore, it is recommended to use an ETL testing tool that provides full coverage of automated testing without manual intervention and can cover all iterative testing processes. The various ETL testing tools are described below:


QuerySurge is an ETL testing solution developed by RTTS. It is specifically designed to automate the storage and testing of large data sets. This ensures that the source information is retained in the target systems.

Data validation in Informatica

Informatica Data Validation is one of the most powerful tools. Integrate archive and integration services with Power-Center. It allows an ETL developer and business analysts to develop guidelines for ETL testing the displayed information.


QualiDi enables customers to reduce costs, increase ROI and accelerate time to market. With this ETL tool, all parts of the testing cycle can be automated. This allows customers to reduce costs, achieve higher ROI and speed time to market.


The tool is used to automate data migration and production testing. Users can identify information issues that may arise during ETL processes. iCEDQ performs verification, validation and reconciliation of source and target systems.

ETL data gap validator

The Data Gaps ETL Validator is a tool for validating data gaps in ETL. It facilitates testing of data integration, data migration and data warehouse projects. It has an integrated ETL engine that can compare millions of documents.

Data-driven testing

The data-driven testing tool performs reliable data validation to avoid errors during conversion, such as data loss or inconsistencies. It compares data between systems and ensures that the data loaded into the target system exactly matches the original system in terms of data volume, data type, format, etc. The data is then validated against the original system.


SSISTester is a system that performs unit and integration testing of the entire ETL process. SSISTester has a good user interface that allows real-time monitoring of test execution. SSISTester facilitates test execution by providing intuitive access to database sources, packages, etc. It has an integrated project model. Test parameters such as the current test, failed tests and results are provided by SSISTester. Test results can be exported in HTML format, making it easy to save and send.

Wrapping Up

Businesses are using the tool to automate the testing process. This will reduce the chances of errors and businesses can get accurate data on the basis of which they can make decision and future plans.

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