Data Warehouse Testing

Make sure the sanctity of your data is preserved and
avoid data related risks.

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Data Warehouse is a repository of historical data that is used by organizations across the world to make educated decisions. Typically, a data pipeline collects data from multiple databases, files, and other data sources, transforms the data and loads it into a data warehouse on a regular interval.

Organizations use reporting tools on top of a data warehouse so that business users, analysts, data scientist, and others can analyze the data for reporting and decision making.

Therefore, to ensure that the business users are making accurate decisions, it is very critical to be sure that data in the data warehouse is correct. So, to achieve that organizations have to test their data warehouse end to end.

What is Data Warehouse Testing?

Data Warehouse Testing-iCEDQ

The process of verifying the entire data warehouse landscape for data integrity and data accuracy is called Data Warehouse Testing. It is a combination of ETL Testing and Business Intelligence (BI) Report Testing.

Data Warehouse Testing Challenges

Below are some of the critical challenges in successfully testing a data warehouse.

Testing high volume of data

Testing high volume of Data-iCEDQ

A typical data warehouse will contain millions of rows and testing high volume of data regularly is very challenging. Moreover, using a sample data set for testing is not at all useful as it will never cover all the scenarios, and users will see data issues in production.

Testing across multiple environments

Testing across multiple environment-iCEDQ

Testing across multiple environments like DEV, QA, UAT, and making sure data is valid across all of these environments is time-consuming and may cause duplication of work.

Inability to identify data issues on time

Inability to identify data issues on time-iCEDQ

Inability to identify data issues on time due to lack of data warehouse test automation. Failure to do so can cause problems in downstream applications.

Comparing data across heterogeneous data sources

Comparing data across heterogeneous-iCEDQ

Comparing data across heterogeneous data sources is another challenge in Data Warehouse Testing. Users have to bring the data in excel or eyeball the data for issue when comparing manually, both of which are likely to cause an error.

Testing a Business Intelligence (BI) report

Testing a Business Intelligence-iCEDQ

Testing a Business Intelligence (BI) Report with a data warehouse or data mart table is not straight forward. Because there is no standard connector for the BI reporting tools, users have to manually export the data from the BI report to validate it. This task again adds much manual effort to testing.

Type of Data Warehouse Testing Checks

Source data validation

Source data validation test is triggered before the staging load process to ensure that there are no data issues in the source system.Verify there are no duplicates in the source system, Make sure only valid and not null values get loaded into staging.

Source to target reconciliation

Source to target reconciliation allows a user to compare all the data as is when minimum or no transformation is involved.

Verify all the data from the source system has been loaded into staging as is.

ETL reconciliation

ETL reconciliation helps user validate any data transformation, conversion, calculation between source and target.

Verify that the ETL process is successfully transforming and loading data from multiple source systems into the Data warehouse based on the mapping rules.

Business validation

Business validation is used to ensure that the data is valid according to the business rule. Verify that the sales amount calculation does not break any business rule

Report reconciliation

Report reconciliation verifies data across two business intelligence report row by row and column by column

Make sure that BI Report is returning the same result in two different environments.

Business reconciliation

Business reconciliation allows a user to audit the data between two business areas for consistency.

Example: Two separate ETL processes are loading Orders and Shipment data. Verify that no shipment data is present for which no orders are present.

Five things to look for in Data Warehouse Testing tool

Testing high volume of Data-iCEDQ

High data volume testing

Considering the volume of data in a data warehouse system, this is an essential feature. It will help users test all the data without any human errors and will increase the organization’s trust in their data systems.

Regression testing-iCEDQ

Regression testing

Data Warehouse processes are complex and many in number. Any small change in a process can cause data issues in downstream systems. Regression testing will ensure that new code change will not break old functionality and at the same time, the new feature is working as expected.

Testing a Business Intelligence-iCEDQ

Business Intelligence Tool Connectivity

There is no standard connectivity protocol for reporting tools, and the ability to test BI Reports is a vital part of Data Warehouse Testing. So, having out of the box connectors for different reporting tools like Tableau, MicroStrategy and others is a must.

CI Tool Integration-iCDQ


A test automation tool which is completely isolated is not practical. Organizations have a variety of tools sets for test management, orchestration, defect management, notification and they want to integrate all of these tools along with test automation tool to get an effective workflow as well as an overall view of the system.



A Data Warehouse testing tool should enable its users to create any custom report as per requirement whenever needed. It should be able to integrate with external reporting tools like Tableau, Power BI, etc.

Report reconciliation-iCEDQ

Report reconciliation

Report reconciliation verifies data across two business intelligence report row by row and column by column

Make sure that BI Report is returning the same result in two different environments.

Use iCEDQ for Data Warehouse Testing

  • Our data validation and comparison engine either works in-memory or runs on Apache Spark engine, allowing users to compare all of their data efficiently without any issues.
  • Users can connect to different data sources, be it a database, file source, or a reporting tool and validate and compare data across them.
  • Build a test suite or a regression pack for regression testing, release management testing, or for reusing the same rules across multiple data warehouse environments.
  • Integrate iCEDQ with your data pipeline orchestration by executing the tests using CLI or Rest API and make decisions based on the test results.
iCEDQ Engine-iCEDQ

Use Cases