Data Warehouse Testing: Checklists, Challenges, and Tools

Data Warehouse Testing

Data warehouse testing is a testing technique in which the data placed inside a data warehouse is tested for accuracy, integrity, reliability, and consistency in accordance with the company's data framework. Data warehouse testing aims to ensure that the data that is integrated inside the data warehouse is reliable enough to make the best decisions for company benefits.

Three basic testing levels like Unit Testing, Integration, System Testing with data warehouse testing checklists are crucial for making data warehouse systems efficient and running them correctly.

Unit Testing

In unit testing, each module, like SQL Script, program, Unix Shell, and the procedure is tested at the developer’s end.

Unit Testing Checklist

• Identify duplicate values generated using sequence generators.
• Check the mapping of fields in data marts that support data staging.
• Review the data type constraints of the fields present in core levels and staging.
• Assure that there are no duplicate records in target tables.
• Check the status of data loading and error messages after the ETL (extracts, transformations, loads) process.
• Verify the correctness of surrogate keys that help uniquely identify data rows.
• Ensure that all specified fields and tables were loaded from source to staging.
• Ensure formats and data types are as set during database design.
• Ensure data transformations are correct as per business rules.
• Check all modifications, errors, data cleansing, and exception handling.
• Check that not-null fields were populated.
• Verify data mappings and stored procedure calculations.
• Check for string columns that are inappropriately left or right trimmed.
• Ensure every ETL session is completed with planned exceptions.
• Check whether no data truncation occurs in each field.
• Verify numeric fields that should be populated precisely.

Integration Testing

The purpose of integration testing is to identify the faults in integrated modules and then confirm whether several components perform well after integration or not. It is a type of testing method in which many individual modules/units of the application are combined and tested against the number of inputs. It also ensures that the ETL workflow will execute as it is scheduled with correct dependencies.

Integration Testing Checklist

• Check for the successful implementation of data-loading workflows.
• Check exception log messages and all ETL errors for correctable issues.
• Ensure data tables are occupied correctly with all expected records.
• Validate data-load jobs that should start and end at predefined times.
• Check all dependencies across data-load workflows, like staging-to-operational data store, source-to-staging, and staging-to-data marts.

System Testing

The testing team performs the system testing with the aim of whether the entire system works correctly together or not. In system testing, the whole data warehouse app is tested together. The size of the data warehouse is immense. It is essential to conduct limited device testing before implementing the test plan. With system testing, you can ensure that the desired business functions will implement correctly. It includes data verification to test the data quality populated into target tables.

System Testing Checklist

• Ensure the system functionality that should fulfill the business specifications.
• Check all database updates, transactions, and data-flow functions for accuracy.
• Verify the business reports functionality.
• Check for end-to-end connectivity of the infrastructure and integration of systems. Check network and hardware configurations that should be correct.
• Review the count of records in source tables, compare their counts in target tables, and analyze the rejected documents.

Data Warehouse Testing Challenges

The process of data warehouse testing is very different from the app testing as it requires a data-centric testing approach while testing. Programmers who face various challenges are as follow:

• Data warehouse testing depends upon the availability of test data and requires several test scenarios.
• In data warehouse testing, sometimes it is essential to compare large volumes of data with millions of records.
• Data should be collected from heterogeneous data sources for comparisons such as flat files and databases.
• Data is always converted, and it may require complicated SQL queries to compare.
• BI tools such as Cognos, OBIEE, Business Objects, and Tableau produce reports on the fly based on a metadata model. Performing testing with numerous combinations of attributes and measures is very challenging.

• A huge volume of reports and data make it very challenging to test for functionality, stress, and regression.

What is the Role of ETL in Data Warehouse Architecture?

Integrating, consolidating, and reorganizing a vast amount of data from a wide variety of sources is crucial when planning the architecture of your data warehouse. ETL (Extract-transform-load) processes are utilized to extract, transform & load data from source systems for cohesive integration. After putting it all together, it is used to create a single business intelligence information source.

ETL is at the core of the data warehouse architecture as a crucial design principle. It ensures that all processes will interact seamlessly, and information tends to flow as defined by the business. One can shape and modify the data when needed or when required as per workflow.

Things that you should consider from an ETL perspective for data warehouse architecture.

• Understand business requirements.
• Identify and keep profiling your data sources.
• Define data extraction methods.
• Create data transformation requirements.
• Decide how you will arrange or manage the ETL process.

Key Aspects of ETL/Data Warehouse Testing

Some key aspects of ETL/data warehouse testing are:

Data transformation testing

Multiple SQL queries are used to implement each and every row and verify data transformation standards. Various rules are verified, which are used in the logic of warehouse functioning, like whether it is processed based on the client’s demands.

Data Quality Testing

Preparing a data warehouse test plan is not enough if the quality is not met. With data quality testing, you can verify the ETL application correctly and replace it from default values to produce the correct data for users. If the data quality poses any issues, the system may use the configured data quality management rules.

Performance Testing

It is essential to upload the data within the expected deadline. If further scalability with a good performance index is the key concern, performance testing will be helpful.

Production Validation Testing

Production validation is also called table balancing or product reconciliation, which helps validate data in production systems and compare the validated data against source data. It protects data against failed loads, faulty logic, or operational processes that are not loaded to the system. When it comes to comparing the data warehouse statistics and initial data, production validation testing is taken into account.

Source to Target Count Testing

It tests information sections, record counts, and other content types & also checks the specification rating value. Similarly, source to target data testing is the process of validating the correct data uploading in the warehouse without quality loss and its size.

Data & Constraint Check

Checking data types, data size, indexing, and constraints is vital when testing the data warehouse systems.

Application Migration Testing

Verification of ETL applications is the next step that one considers while migrating them to a new platform. To ensure that the ETL application is migrated successfully from one platform to another, application migration testing is carried out.

Duplicate Data Checking

Checking duplicate data is another data warehouse testing strategy that helps verify the identical information in target systems. If the duplicate data is not checked, it can cause incorrect analytical reports when you use more of the warehouse.

Data-Centric Testing

Data-centric testing is about testing the quality of data. This type of testing aims to ensure the correct and valid data in the system. It assures proper ETL methods applicable to the source database while transforming and loading data in the target database. Upgrades are easy to perform, and you can get an assurance of proper system migration with data-centric testing.

Business Testing

With business testing, you can ensure that the data will fulfill the crucial business requirements. Testers can evaluate the data against the stated business rules. It also checks whether data has been copied, moved, or loaded accurately and completely.

Data Accuracy Testing

It is a type of ETL testing that ensures the data is transformed correctly and loaded according to expectation. Through testing, you can identify the errors that are obtained due to improper mapping of columns, truncation of characters, and implementation of errors in logic.

Data Completeness Testing

Data completeness testing helps to verify expected data that is loaded in target from the source. Verification of counting rows and matching the counts in the reference table like requirements can quickly be done with data completeness testing.

Data is vital for all industries to make critical decisions. ETL testing is beneficial for validating and verifying business data and ensuring that the business information is consistent and reliable. With ETL testing, achieving high-quality data is possible. Due to the continuously changing demands of organizations, ETL testing helps make changes in the data warehouse schema and load the data effectively. As a tester, if you want to have the best data warehouse test plan or strategy, you need to understand the methods for production and testing & define them clearly. You should perform impact-analysis for ETL and Data Warehouse testing and focus on good collaboration between operations, development, and business teams.

Also Read: Is ETL Testing Really Important For Your Business?

ETL/ Data Warehouse Testing Tools

ETL or data warehouse testing can be performed using tools like Informatica or with manual ways. Much of the ETL testing you can do on spreadsheets or by SQL scripting. Unlike other automation testing tools, there are various benefits of performing ETL testing with automated tools as it ensures the delivery of reliable data in your production systems. Moreover, automated data warehouse testing tools allow you to perform various types of testing, such as unit testing, continuous integration, regression, functional testing, and many more. With automated ETL/data warehouse testing, you can reduce a lot of time, and it helps you maintain accuracy from start to end.

Some Popular ETL Testing Tools

• Informatica Data Validation
• RightData
• QuerySurge
• Xplenty
• Talend Open Studio for Data Integration
• Datagaps ETL Validator
• TestBench
• DbFit

Data Warehouse Testing Automation

Data warehouse testing automation is defined as the use of tools for controlling the implementation of tests and comparing the actual outcomes to predicted outcomes. It allows us to set up the preconditions for tests, control the test & manage the test reporting functions. Automating a manual method that is already in place and used by a formalized testing process is called test automation.

You can no doubt perform ETL tests manually, but it is very time-consuming and gives you a laborious feel while identifying defects from data. It is not as effective as an automated data warehouse testing strategy because test automation of data warehouses includes writing of programs for testing that you can run n number of times at an affordable price rate.

For ETL and database projects, the latest automated testing tools are suitable for software development that you may find it hard to adopt because large volumes of data make even automated testing a challenging job. Furthermore, complications can increase when you focus on a wide variety of data warehouse architectures as it involves two more data databases that require a professional data warehouse tester who has knowledge and experience of special coding for data extraction, transformation, cleaning, loading, data aggregation, and data enrichment.

Test automation software can be costly. However, it can become cost-effective when you consider regression testing for the repeated test actions.

Conclusion

From data extraction, data transformation, data cleaning to data loading, every single task of data warehouse testing is critical to compete in business. You must have the right information and analysis at your fingertips because it can help you make a difference for companies and make the best decisions for their effective growth. To overcome the challenges of data warehouses that may occur while extraction, transformation, and loading processes, you need to consult with the right data warehouse/ETL testing teams to produce desirable results for you.

Get Data Warehouse & ETL testing services from us today. We understand that stability and data security are a critical concern for every business. We provide you a comprehensive vision of your data status with end-to-end specialized data warehouse testing approaches and give benefits like zero production defects, advanced ETL test coverage, improved security, efficient level of testing, and help you bring your product to market faster. For long discussions regarding ETL/Data warehouse testing, contact us.