ETL Testing Interview Questions

ETL Testing Interview Questions, ETL testing process, different types of ETL testing, ETL test tools, data warehouse, and ETL Test Life Cycle.

ETL Testing Interview Questions and Answers

1. What is ETL Testing?

ETL — Extract/Transform/Load — is a process that extracts data from source systems, transforms the information into a consistent data type, then loads the data into a single depository. ETL testing refers to the process of validating, verifying, and qualifying data while preventing duplicate records and data loss.

2. What is the importance of ETL testing?

ETL Testing holds great significance in the process of data warehousing and data migration as it validates the efficient loading of data from the source system to the data warehouse.

  • It helps in Identifying Problems with the Source Information
  • It Facilitates the Transfer of Bulk Data.
  • It Prevents Loss of Data and Duplication of Records.
  • It Eliminates Possible Errors in Transmission of Information across an Enterprise

3. Explain the process of ETL testing.

Stages of the ETL testing process?

  • Identify your business requirements.
  • Assess your data sources.
  • Create test cases.
  • Begin the ETL process with the extraction.
  • Perform the necessary data transformation.
  • Load the data into the target destination.
  • Document your findings.
  • Conclude testing and proceed with ETL.

4. Name some tools that are used in ETL.

Best ETL Tools are:

  1. Hevo Data
  2. Pentaho
  3. Talend
  4. AWS Glue
  5. Informatica PowerCenter
  6. Apache Nifi
  7. Azure Data Factory
  8. IBM Infosphere DataStage
  9. Blendo
  10. StreamSets
  11. Google Data Flow
  12. Xplenty
  13. IRI Voracity
  14. Xtract.io
  15. Jaspersoft
  16. DB Software Laboratory
  17. Sybase ETL
  18. SAS Data Integration Studio
  19. SAP BusinessObjects Data Integrator
  20. Skyvia

5. What are the different types of ETL testing?

There are mainly 8 Types of ETL Testing:

  • Production Validation
  • Source to Target Testing
  • Metadata Testing
  • Performance Testing
  • Data Transformation Testing
  • Data Quality Testing
  • Data Integration Testing
  • Report Testing

6. What are the roles and responsibilities of an ETL tester?

An ETL tester is responsible for validating data sources, extracting data applications of transformation logic, and uploading data in target tables. They are responsible for designing, testing, and troubleshooting the company’s data storage system before it goes live.

7. What are the different challenges of ETL testing?

The main challenges of ETL or Data warehouse testing:

  • Data loss during ETL testing.
  • Duplicate data and Incompatibility.
  • Lack of inclusive testbed.
  • Testers have no benefits to executing ETL jobs on their own.
  • Data volume and complexity are huge.
  • Inefficient procedures and business processes.
  • Inconvenience securing and building test data.
  • Absence of business course information.

8. What is the difference Between ETL Testing and Database Testing?

ETL Testing:
ETL Testing verifies whether data is moved as expected, verifies for the counts in the source and target are matching, verifies the foreign primary key relations are preserved during the ETL, and verifies for duplication in loaded data.

Database Testing:
Database Testing verifies if the data is following the rules/ standards defined in the Data Model, verifies that there are no orphan records and foreign-primary key relations are maintained, verifies that there are no redundant tables and the database is optimally normalized, and verify if data is missing in columns where required.

9. What is transformation?

A transformation is a repository object which generates, modifies or passes data. Transformation is two types Active and Passive.

10. What is the three-layer architecture of an ETL cycle?

The three layers in the ETL are:

Staging Layer:
The staging layer is used to store the data which is extracted from the different data source systems.

Data Integration Layer:
The integration layer transforms the data from the staging layer and moves the data to a database. In the database, the data is arranged into hierarchical groups, which is often called dimension, and into facts and aggregation facts. The combination of facts and dimension tables in a data warehouse system is called a schema.

Access Layer:
The access layer is used by the end-users to retrieve the data for analytical reporting.


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