In the current digital age, data is considered the new oil since it provides insights and is very useful while making business decisions. While data can be enhanced by processing, analyzing, and transforming, it can be lost, susceptible to errors, inaccurate, or deficient. Flaws in data may impact business decisions. For this reason, ETL and ETL testing are important in database architecture. ETL Testing is carried out to ensure that the data after extraction, transformation and loading has reached the right designation in the proper format. 

Prepare for relevant opportunities with some of the top ETL Testing interview questions.

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Top ETL Testing Interview Questions and Answers

1. What is ETL?

Extract Transform and Load (ETL) refers to the extraction of data from varied data sources, transforming the data so that it is in a usable format and loading the data into repositories through which end-users can access and utilize it.

2. What is ETL Testing?

ETL Testing refers to the process of validating, verifying, and qualifying data while avoiding duplication and preventing data loss. This process is important to ensure that data is delivered securely to the right destination and that the data quality is impeccable before its usage in BI (Business Intelligence) reports.

3. What are the steps in the ETL Testing process?

1.  Understand Business Requirements

2.  Identify Data Sources

3.  Develop Test Cases

4.  Authenticate Extracted Data

5.  Validate Data Transformation

6.  Verify the Data Loaded

7.  Execute Test

8.  Prepare Summary Report & Result Analysis

9.  Test Closure

4. Name the different types of ETL Testing. 

1.  Production Validation

2.  Source to Target Count Testing

3.  Source to Target Data Testing

4.  Metadata Testing

5.  Performance Testing

6.  Data Transformation Testing

7.  Data Quality Testing

8.  Data Integration Testing

9.  Report Testing

5. Explain the terms data warehousing and data mining.

Data warehousing involves gathering a huge amount of data from different data sources for decision-making. Data mining refers to the extraction of meaningful patterns from the available data.

6. What is partitioning?

Partitioning helps manage database objects better by dividing the storage area and organizing the data more conveniently. When the data warehouse is partitioned, finding and accessing data is faster.   

7. Describe the 3-layer architecture in the ETL cycle.

The 3 layers involved in the ETL cycle are:

1.  Staging Layer is the layer that stores data extracted from multiple sources.

2.  Data Integration Layer is the layer that transforms the data and moves it from the staging layer to the database layer.

3.  Access Layer is the layer used by the end-user to recover data that can be used for analytical reporting.

8. Compare Star Schema and Snowflake Schema.

Both Star and Snowflake Schemas are multidimensional models. Star Schema comprises fact and dimensional tables and has a star-shaped formation. On the other hand, the Snowflake Schema consists of facts tables, sub-dimension tables, and three-dimension tables put together in a formation that resembles a snowflake. While Star Schema is essentially a top-down model, Snowflake schema is a bottom-up model. Compared to Star Schema, the Snowflake schema is more complex, has more foreign keys, and has lesser repetition.

9. Explain what you understand by the term Grain of Fact.

Grain of Fact is the term used to explain the fact level at which information is stored in the fact table.

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10. What is Data Purging?

Data purging is the process of permanently deleting and removing data from the data warehouse. Eliminating unwanted data frees up storage and memory space.  

11. What is a factless table?

A factless table is a table that does not have any facts or measures. Its purpose is to demonstrate relationships between dimensions. A factless table does not hold text or numeric data.

12. What is Slowly Changing Dimensions (SCD)?

Slowly Changing Dimensions (SCD) are dimensions that store and manage current and past data in a data warehouse. This data in SCD changes very slowly over time and does not change as per any predefined schedule. 

13. What is a data source view?

A data source view defines the relational schema that is used to carry out analysis in the databases. Cubes and dimensions can also be created using the data source view instead of being built from data source objects. This allows users to construct dimensions inherently and offers superior control over the data structures.

14. Explain what is meant by data cleansing.

Data cleansing is the process of finding and eliminating corrupt and erroneous data along with errors in data due to duplication, and insufficiency. It is also known as data cleansing or scrubbing.

15. What is BI (Business Intelligence)?

Business Intelligence refers to gathering, storage, and analysis of data with the objective of converting raw data into actionable information which can be used to make better business decisions.

16. What is the Importance of ETL Testing?

ETL testing has several significant advantages, including the following:

  • Make sure data is transferred from one system to another swiftly and efficiently
  • ETL testing can also detect and avoid data quality concerns, such as duplicate data or data loss, during ETL processes
  • Verifies the ETL process is not hindered and is functioning smoothly
  • Accurate output is guaranteed by ensuring that all data is implemented following client specifications
  • To guarantee a seamless, error-free transfer

17. When Do We Need the Staging Area in the ETL Process?

An intermediary between the data sources and data warehouse/data mart systems is known as a "storage area." It serves as a temporary repository for data during the data integration process. Data is cleansed and verified for a duplicate in the staging area. The staging area is intended to provide a variety of advantages, but its primary purpose is to be used. As a result, it is utilized to boost productivity by ensuring the integrity and quality of the data.

18. What Are OLAP Cubes And Cubes?

Fact tables and dimensions from the data warehouse make up the building blocks of a cube. It provides numerous analytical choices.

Data gets stored in the multidimensional form in an OLAP cube, which stands for Online Analytical Processing. It's made up of facts known as measurements, which are arranged into categories based on their dimensions.

19. What Are the Tasks And Roles of an ETL Tester?

ETL testers are in high demand because of the importance of ETL testing. ETL testers verify data sources, extract data, apply transformation logic, and load data into target tables. ETL testers are expected to do the following tasks:

  • Proficiency of ETL tools and methods in depth
  • Thorough ETL program testing
  • Examining the data warehouse test component
  • Perform the data-driven test on the backend
  • Identifies problems and recommends the best possible solutions
  • Approve and review the specifications and requirements
  • The method of creating SQL queries to test various situations
  • Primary keys, defaults, and other ETL-related functions 
  • Consistently evaluates product performance

20. Explain data mart.

It is possible to create "data marts" within an enterprise data warehouse to better serve the needs of specific business units or departments. Using a data mart, you can quickly and readily access a subset of a data warehouse's data by a broader range of users than would otherwise be possible. 

In some cases, a company may have a data mart that is linked to the company's purchasing, sales, or inventory functions.

21. What Do You Mean by ETL Pipeline?

Because of its sheer importance, you must expect some ETL pipeline questions in your ETL testing interview questions. ETL pipelines, as the name implies, are the means through which ETL processes are carried out. Some functions and activities are necessary to move data from several sources into the data warehouse for analysis, reporting, and data synchronization. 

Some of the advantages include:

  • ETL pipelines can gain a competitive edge
  • It makes it easier to move data from old systems to new ones
  • It reduces errors, slowdowns, and latency, ensuring that information flows smoothly between systems
  • Data can be easily accessed and used by analysts and decision-makers thanks to the ETL pipeline because it centralizes and standardizes data

22. What Do You Mean by ODS (Operational Data Store)?

ODS is a depository for data between the staging area and the Data Warehouse. ODS will load all of the data into the EDW as soon as the data is entered into ODS (Enterprise data warehouse). 

ODS primarily benefits corporate operations by delivering current, clean data from various sources in a single, centralized location. Customers cannot make changes to an ODS database since it is read-only.

23. What Is a Staging Area, And What Is Its Primary Function?

In the ETL process, an intermediary storage space called a staging area or landing zone is utilized. It serves as a buffer between the data source and the data warehouse. When extracting data, staging zones get used mainly to minimize the influence of their respective data sources. 

After data has been loaded, it is transformed, checked, and cleaned in the staging area using several data sources.

24. Explain ETL Mapping Sheets.

Every column in the source and destination tables and their lookup in reference tables are often included in ETL mapping sheets. 

Big queries with several joins may be necessary during ETL testing at any point in the testing process to ensure that the data is correct. ETL mapping sheets make it substantially easier to write data verification queries.

25. How Is ETL Testing Utilized in the Handling of Third-Party Data?

Vendors of all stripes create a diverse range of software for large corporations. As a result, no single vendor is responsible for the entire process. To illustrate, imagine a telecom project in which one business handles invoicing, and another handles customer relationship management (CRM). 

Data feeds from other companies can now be received by CRMs, for example, if a CRM needs billing information from another company. As a result, you can use the ETL method to load data from the feed.


1. Name some of the ETL test cases.

1.  Structure Validation

2.  Validate Constraints

3.  Null Validation

4.  Data Validation Check

5.  Data Consistency Check

6.  Duplicate Validation

2. Which are the ETL partition types?

1.  Round-robin Partitioning

2.  Hash Partitioning

3. What is a data mart?

A data mart is a subset of a data warehouse that is dedicated to a business function or department. It makes it easier for users to retrieve specific data rather than searching the entire data warehouse.

4. What is a materialized view?

A materialized view is a database object that includes the result of a query. It pre-calculates the result and makes data fetching faster.

5. What are Schema Objects?

Schema objects are database objects such as tables, views, indexes, synonyms, database links, etc. These are the logical structures created by users that can either hold data or definitions and are arranged in models such as star or snowflake schema.  

6. What is a Bus Schema?

Bus Schema primarily identifies dimensions across various data marts.

7. What is a workflow?

A set of instructions that explains how to execute a task is referred to as a workflow.

8. What is a session?

A session refers to the set of instructions that describes the flow of data from source to destination.

9. What do you understand from the term Mapping?

Mapping depicts the flow of data from source to target.

10. Describe the term Mapplet in brief.

A mapplet is a reusable object that creates and builds a set of transformations.

11. Explain the term worklet.

A set of tasks is referred to as a worklet.

12. Name some of the ETL tools available.

1.  Informatica PowerCenter

2.  IBM InfoSphere DataStage

3.  Oracle Data Integrator

4.  Talend Open Studio

5.  SAP Data Services

6.  SAS Data Manager

7.  Microsoft SQL Server Integration Services

8.  Hadoop

9.  Pentaho Data Integration

10.  Azure Data Factory

13. What are the types of facts in ETL?

1.  Additive

2.  Semi-Additive

3.  Non-Additive

14. Name some of the ETL bugs.

1.  Calculation Bug

2.  Input/Output Bug

3.  Source Bug

4.  Version Control Bug

5.  User Interface Bug

6.  Load Condition Bug

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15. How many fact tables are there in Star Schema?


16. What are the modules in Power Mart?

1.  PowerMart Designer

2.  Server

3.  Server Manager

4.  Repository

5.  Repository Manager 

17. What are the 5 stages of ETL testing?

ETL testing consists of five stages:

  • Determining data sources and requirements
  • Data acquisition
  • Implementing Business logic and Dimensional Modeling
  • Compile and populate data
  • Create Reports

18. What are the three layers in ETL testing?

Typical ETL testing for data warehouses use staging areas, data integration layers, and access layers to accomplish their tasks.

19. What is ETL in testing?

ETL means extracting data from a source system, transforming it into a standard data type, and loading it into a single repository.

20. Is SQL required for ETL testing?

Yes. When performing ETL testing, you often compare tens of millions of records. It is necessary to test data from a variety of sources. Complex SQL queries may be required when comparing the data because of the frequent transformation of the data.

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