ETL Testing: A Comprehensive Guide for 2024

In the modern world today, companies gather data from multiple sources for analysis. This data can be further processed by various BI Tools to gather valuable business insights or stored in a Data Warehouse for later use. In order to do any of this, the process of ETL Testing is required. ETL Testing is derived from the original ETL process.

ETL stands for Extract, Transform and Load and is the primary approach Data Extraction Tools and BI Tools use to extract data from a data source, transform that data into a common format that is suited for further analysis, and then load that data into a common storage location, normally a Data Warehouse. ETL Testing comes into play when the whole ETL process needs to get validated and verified in order to prevent data loss and data redundancy.

This article focuses on providing a comprehensive guide on ETL Testing. It will talk about the process of ETL Testing, its types, and also some challenges. It also explains the potential of Testing Tools. Read along to find out about this interesting process.

What is ETL?

ETL stands for Extract, Transform and Load and is the process of integrating data from multiple sources, transforming it into a common format, and delivering the data to a destination usually a Data Warehouse for gathering valuable business insights.

What is ETL Testing?

ETL Testing is a process of verifying the accuracy of data that has been loaded from source to destination after business transformation. The verification of data takes place at multiple stages during the ETL process.

When do we need ETL Testing?

Here are a few situations where ETL Testing can come in handy:

• Following a data integration project.
• Following a data migration project.
• When setting up a data warehouse for the first time, after the data gets loaded.
• After adding a new data source to your current data warehouse.
• When migrating data for any reason.
• In case there are any suspected issues with the performance of ETL processes.
• If there are any suspected issues with data quality in any of the source systems or the target system.

Difference Between Database Testing and ETL Testing

ETL Testing	Database Testing
It verifies whether data is moved as expected.	It checks if the data is following the rules/ standards defined in the Data Model.
It verifies for the counts in the source and target are matching.	It verifies that there are no orphan records and that foreign-primary key relations are maintained.
Verifies that the foreign primary key relations are preserved during the ETL.	Verifies that there are no redundant tables and database is optimally normalized.
Verifies for duplication in loaded data.	Verify if data is missing in columns where required.

What are the 8 stages of the ETL Testing Process?

ETL Testing is the process that is designed to verify and validate the ETL process in order to reduce data redundancy and information loss. As Testing is a vague concept, there are no predefined rules to perform Testing.

However, the ETL Testing process can be broken down into 8 broad steps that you can refer to while performing Testing:

• Gathering Business Requirements
• Identifying and Validating Data Sources
• Creating Test Cases
• Executing Test Cases
• Creating Reports
• Re-testing Bugs
• Preparing Reports
• Closing the Reports

1) Gathering Business Requirements

The first and foremost step in ETL Testing is to know and capture the business requirement by designing the data models, business flows, schematic lane diagrams, and reports. It’s important to understand business requirements so that the tester can be aware of what is being tested. The team should thoroughly document the scope of the project so that the tester can fully understand it.

2) Identifying and Validating Data Sources

The next step is to identify the source data and perform preliminary checks like schema check, counts, validation of tables, etc. to check whether the ETL process aligns with the business model specification. In this stage, the primary keys are checked as per the model and care is taken to prevent any duplicate data otherwise it will lead to inaccurate aggregation.

3) Creating Test Cases

The next step is to create a source-to-target mapping and to design the test cases, including transformation according to business test cases, SQL scripts to perform the source-to-target comparison, execution flows, etc. Then, validate the documents against the business requirements to ensure it aligns with business needs. 

4) Executing Test Cases

The next step involves executing the created test cases on the QA (Question-Answer) environment to identify the types of bugs or defects encountered during Testing. Analysts must try to reproduce the defect and log them with proper comments and screenshots. The ETL tests must be executed as per business requirements.

5) Creating Reports

After logging all the defects onto Defect Management Systems (usually JIRA), they are assigned to particular stakeholders for defect fixing. Analysts must ensure that they have captured all the relevant screenshots, mentioned steps to reproduce the test cases, and the actual vs expected results for each test case.

6) Re-testing Bugs

Once the developer fixes the bug, the bug is tested in the same environment again to ensure there are no traces of the bug left. Also, Regression Testing is performed to ensure there are no new bugs introduced while fixing the earlier one.

7) Preparing Reports

Reports are prepared based on the bugs and test cases and are uploaded into the Defect Management Systems. The report will help the stakeholders to understand the bug and the result of the Testing process in order to maintain the proper delivery threshold.

8) Closing the Reports

The last step involves, closing the reports once everything is completed by mentioning proper comments and attaching relevant files related to the test cases and the business requirements.

The whole Testing process is depicted in the figure below:

What are the ETL Testing Techniques?

ETL Testing is designed to ensure that the data which has been loaded from a data source to a destination after transformation is accurate. It involves the verification of data at various stages, which is used between source and destination.

As you saw in the general process of Testing, there are mainly 12 ETL Testing types:

• Production Validation Testing
• Source to Target Data Testing
• Metadata Testing
• Performance Testing
• Data Transformation Testing
• Data Quality Testing
• Data Integration Testing
• Report Testing
• Application Migration Testing
• Duplicate Data Check
• Source to Target Count Testing
• Data and Constraint Check

1) Production Validation

It is a table reconciliation or product balancing technique, which usually validates the data in the target systems, i.e. production system data, and compares it against source data.

2) Source to Target Testing

In this type of ETL Testing, the count from source systems is tallied to check if it matches the target system’s expected records. It also validates the data’s completeness, i.e. it checks the loss/truncation of the data in the target systems.

3) Metadata Testing

Metadata Testing involves matching schema, data types, length, indexes, constraints, etc. between source and target systems.

4) Performance Testing

Performance Testing tests the systems’ performance which determines whether data is loaded within expected time frames to the systems and how it behaves when multiple users logs onto the same system.

5) Data Transformation Testing

In this type of Testing, SQL queries are run to validate business transformations and it also checks whether data is loaded into the target destination with the correct transformations.

6) Data Quality Testing

In this type of ETL Testing, checks are performed on the Data Quality. This includes invalid characters, patterns, precisions, nulls, and numbers from the source, and invalid data is reported.

7) Data Integration Testing

This type of Testing confirms that all the sources’ data has been loaded to the target Data Warehouse correctly and it also checks threshold values.

8) Report Testing

This type of ETL Testing, reviews data in the summary report, verifies whether the layout and functionality are as expected, and makes calculations for further analysis.

9) Application Migration Testing

In this ETL Testing technique, you need to make sure that the ETL application is working fine on migrating to a new platform or box.

10) Duplicate Data Check

Here, you need to check if there is any duplicate data present in the target system. This is because duplicate data might lead to incorrect analytical reports.

11) Source to Target Count Testing

Here, you need to make sure that the count of records loaded within the target is matching with the expected count.

12) Data and Constraint Check

In this technique, the datatype, index, length, constraints, etc. are tested.

What are the ETL Testing Challenges?

Although ETL Testing is a very important process, there can be some challenges that companies can face when trying to deploy it in their applications. Some of those challenges are given below:

• Frequent changes in the requirement of the customers cause re-iteration of test cases and execution.
• Failure to understand business requirements or employees are unclear of the business needs.
• Due to changes in requirements by the customer, a tester might need to re-create/modify mapping documents and SQL scripts, which leads to a slow process.
• Data loss can occur during migration because of which it is hard to perform source-to-target reconciliation.
• Change in the data source or incomplete/corrupt source data.
• Real-time data may impact the reconciliation process between data sources and target destinations.
• The huge volume of historical data may cause memory issues in the system.
• Using inappropriate Testing tools.
• Having an unstable Testing environment.

Capabilities of an ETL Testing Tool

Not all the tools can be applied to every user’s needs. Every Testing team has different requirements, and thus it is important to choose the ETL Testing tool to avoid future bottlenecks carefully. Some of the parameters to consider when choosing an ETL Testing Tool are given below. Find out the difference between ETL vs ELT here.

• Cloud Compatibility: The ETL Tool should have built-in compatibility with Cloud systems such as Amazon Web Services, Google Cloud Platform, Salesforce, Snowflake and more.
• User Interface: A good UI solves many problems. The Testing Tool should have a nice UI to display the results and reports. It also helps users to view the data and execute SQL queries against the data.
• Built-in Connectors: An ETL Testing Tool should have in-built connectors to connect source and target datasets to carry out Testing. It should also have the capability to read different file formats like CSV, XML, JSON, etc.
• Automatic Code Generation: A good Testing Tool must have the Automatic Code Generation ability. This is because this can speed up the development process and reduce the chances of human error.

What are the ETL Test Scenarios and Test Cases?

Test Scenario	Test Cases
Mapping doc validation	It Verifies for mapping doc whether the corresponding ETL data is provided or not. Change log should maintain in every mapping doc.
Validation	Validates the source and target table structure with the mapping doc. The data type for the source and target should be the same. The source and target should have equal lengths of data type. Verify that data field types and formats are specified. The data type length of the source should not be less than the data type length of the target. Validate the name of columns in the table against the mapping doc.
Constraint Validation	It ensures that the constraints defined for each table are correct.
Data consistency issues	The data type and length for a particular attribute may vary in files or tables though the semantic definition is the same.
Completeness Issues	It ensures that all data is loaded into the target table. Compare record counts between source and target. Check for any rejected records. Check data should not be truncated in the column of target tables. Compares unique values of key fields between data loaded to WH and source data.
Transformation	This test case would handle all scenarios related to data transformation for your data repository.
Correctness Issues	Data that is misspelled or inaccurately recorded.Null, non-unique, or out-of-range data.
Data Quality	Number check: Need to number check and validate it Date Check: They have to follow date format and it should be the same across all records Precision Check Null check
Null Validate	Verify the null values, where “Not Null” is specified for a specific column.
Duplicate Check	Needs to validate that the unique key, primary key, and any other column should be unique as per the business requirements are having any duplicate rows. Check if any duplicate values exist in any column which is extracted from multiple columns in the source and combined into one column. As per the client’s requirements, needs to be ensured that no duplicates in a combination of multiple columns within the target only.
Data Validation	To know the row creation dateIdentify active records as per the ETL development perspective Identify active records as per the business requirements perspective Sometimes based on the date values the updates and inserts are generated.
Complete Data Validation	To validate the complete data set in the source and target table minus a query in the best solution We need to source minus target and target minus source If the minus query returns any value those should be considered as mismatching rows Needs to match rows among source and target using intersect statementThe count returned by intersecting should match with individual counts of source and target tables If minus query returns of rows and count intersect is less than source count or target table then we can consider as duplicate rows exist.
Data Cleanness	Unnecessary columns should be deleted before loading into the staging area.

What are the Types of ETL Bugs?

Type of Bugs	Description
User interface bugs	The bugs that are related to the GUI of the application such as colors, alignment, spelling mistakes, navigation, etc.
Equivalence Class Partitioning (ECP) bugs	It involves valid and invalid types.
Input and Output bugs	The correct values are accepted the rest are rejected.
Calculation bugs	Mathematical calculation bugs or any wrong output.
Load Condition bugs	It does not allow multiple users and expected load.
Race Condition bugs	System interrupts or crashes.
Version control bugs	Version information is not available.
H/W bugs	The device is not responding to the application.
Help Source bugs	Mistakes in reference documents.

What are the Responsibilities of an ETL Tester?

The primary responsibilities of an ETL Tester can be classified into one of the following three categories:

• Applying Business Transformation Logic
• Stage SFS/ Table or MFS
• Target Table Loading from Stage Table or File after Applying a Transformation

Here are a few pivotal responsibilities of an ETL Tester:

• Test components of ETL Data Warehouse.
• Test ETL software.
• Data transfers and Test flat files.
• Execute backend data-driven test.
• Approve design specifications and requirements.
• Writing SQL queries for Count Test-like scenarios.
• Identify the Problem and offer solutions for potential issues.
• Design, create and execute test plans, test cases, and test harnesses.

Conclusion

This article gave a comprehensive overview of ETL Testing. It explained the process of Testing, its types, and some of its challenges. It also gave some parameters that companies can consider when opting for a good ETL Testing Tool. Overall, Testing plays an important part in governing the ETL process and every type of company must incorporate it in their business.

In case you want to set up an ETL procedure, then Hevo Data is the right choice for you! It will help simplify the ETL and management process of both the data sources and the data destinations.

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