Basic ETL Testing Interview Questions – Complete Beginner-Friendly & SQL-Focused Guide

1. Introduction

If you are preparing for your first ETL or Data Warehouse testing interview, interviewers usually start with basic ETL testing interview questions. These questions test whether you understand:

• ETL fundamentals
  
• Data warehouse flow
  
• Simple SQL queries for data validation
  
• Real-time data issues (missing records, null values, mismatches)
  

Even at a basic level, companies expect logical thinking and SQL skills, not just theory.

This article is written especially for:

• Freshers
  
• Manual testers moving to ETL testing
  
• QA professionals with 0–2 years of ETL exposure
  

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2. What is ETL Testing? (Definition + Example)

ETL Testing is the process of validating data that is:

• Extracted from source systems
  
• Transformed using business rules
  
• Loaded into a target data warehouse or data mart
  

Simple Real-World Example

• Source: Orders table from an application database
  
• Transform:
  
  • Remove duplicate orders
    
  • Convert currency
    
  • Calculate total sales
    
• Load: fact_sales table in Data Warehouse
  

ETL testing ensures:

• No data loss
  
• Correct transformation
  
• Accurate reports

3. Data Warehouse Flow – Source → Staging → Transform → Load → Reporting

Typical ETL Architecture (Basic Level)

1. Source Systems – OLTP databases, files, APIs
   
2. Staging Area – Raw extracted data
   
3. Transformation Layer – Business logic & cleansing
   
4. Target (DW/Data Mart) – Fact & Dimension tables
   
5. Reporting Layer – BI tools
   

👉 Interviewers often ask: “What validations do you perform at each stage?”

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4. Basic ETL Testing Interview Questions & Answers (Beginner → Intermediate)

A. Fundamental ETL Testing Interview Questions

Q1. What is ETL?
ETL stands for Extract, Transform, Load.

Q2. What is ETL testing?
ETL testing validates that data is correctly extracted, transformed, and loaded into the target system.

Q3. Why is ETL testing important?
Incorrect ETL data leads to wrong business reports and decisions.

Q4. What is a data warehouse?
A centralized repository that stores historical and integrated data for analysis.

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B. Data Warehouse Concepts

Q5. What is a staging table?
A temporary table that stores raw extracted data before transformation.

Q6. What is a fact table?
Stores measurable business data such as sales, revenue, quantity.

Q7. What is a dimension table?
Stores descriptive data such as customer, product, and time.

Q8. What is Star Schema?
A schema where a fact table is connected to multiple dimension tables.

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C. Source-to-Target (S2T) Mapping Questions

Q9. What is Source-to-Target (S2T) mapping?
A document that defines how source fields map to target fields with transformation rules.

Q10. Why is S2T mapping important?
It acts as a blueprint for ETL development and testing.

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5. SQL Query Examples for Basic ETL Testing

Record Count Validation

SELECT COUNT(*) FROM src_orders;

SELECT COUNT(*) FROM fact_orders;

✔ Ensures no data loss during ETL.

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Data Validation Using JOIN

SELECT s.order_id,

       s.amount AS src_amount,

       t.amount AS tgt_amount

FROM src_orders s

JOIN fact_orders t

  ON s.order_id = t.order_id

WHERE s.amount <> t.amount;

✔ Finds mismatched records.

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Finding Missing Records

SELECT s.order_id

FROM src_orders s

LEFT JOIN fact_orders t

  ON s.order_id = t.order_id

WHERE t.order_id IS NULL;

✔ Identifies records not loaded into target.

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GROUP BY Aggregation Validation

SELECT region, SUM(sales_amount)

FROM fact_sales

GROUP BY region;

✔ Validates aggregation logic.

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Window Function (Basic Awareness)

SELECT customer_id,

       SUM(amount) OVER (PARTITION BY customer_id) AS total_spend

FROM fact_orders;

✔ Used for running totals.

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6. Slowly Changing Dimension (SCD) – Basic Questions

Q11. What is SCD?
SCD stands for Slowly Changing Dimension.

Q12. What is SCD Type 1?
Old data is overwritten; no history maintained.

Q13. What is SCD Type 2?
History is maintained using start date, end date, and active flag.

SCD2 Validation Query

SELECT customer_id, start_date, end_date, is_active

FROM dim_customer

WHERE customer_id = 101;

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7. Scenario-Based Basic ETL Testing Interview Questions

Scenario 1: Record Count Mismatch

Possible Reasons:

• Filter condition mismatch
  
• Duplicate records in source
  
• Incorrect join
  

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Scenario 2: Null Values in Target

SELECT *

FROM dim_customer

WHERE email IS NULL;

✔ Check default value or reject logic.

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Scenario 3: ETL Job Takes More Time

Basic Checks:

• Table size
  
• Index availability
  
• Query complexity
  

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8. ETL Tools Awareness (Basic Level)

At a basic level, interviewers expect tool awareness, not mastery.

Common ETL tools:

• Informatica
  
• Microsoft SSIS
  
• Ab Initio
  
• Talend
  
• Pentaho
  

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9. Basic ETL Defect Examples + Test Case Sample

Common ETL Defects (Beginner Level)

Defect Type	Example
Data loss	Missing records
Transformation error	Wrong calculation
Duplicate data	Bad join
Null values	Missing default values

Sample ETL Test Case

Field	Value
Test Case ID	ETL_BASIC_TC_01
Scenario	Record count validation
Source	src_orders
Target	fact_orders
Expected	Counts match

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10. Quick Revision Sheet (Basic ETL Testing)

• ETL = Extract + Transform + Load
  
• Always validate count + data + transformation
  
• SQL basics are mandatory
  
• Understand SCD1 & SCD2
  
• Think from data accuracy perspective
  

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11. FAQs – Basic ETL Testing Interview Questions

Q1. Is ETL testing difficult for beginners?
No, basic SQL and logical thinking are enough.

Q2. Is coding required for ETL testing?
No coding, but SQL is mandatory.

Q3. Is ETL testing manual or automated?
Mostly SQL-driven manual testing.

Q4. What is the most important ETL testing skill?
Understanding data flow and writing SQL queries.