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:
- Hevo Data
- AWS Glue
- Informatica PowerCenter
- Apache Nifi
- Azure Data Factory
- IBM Infosphere DataStage
- Google Data Flow
- IRI Voracity
- DB Software Laboratory
- Sybase ETL
- SAS Data Integration Studio
- SAP BusinessObjects Data Integrator
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 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 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:
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.
The access layer is used by the end-users to retrieve the data for analytical reporting.
11. What is a Data Mart?
A data mart is a simple form of data warehouse focused on a single subject or line of business. With a data mart, teams can access data and gain insights faster, because they don’t have to spend time searching within a more complex data warehouse or manually aggregating data from different sources.
12. How do we use ETL in Data Warehousing?
ETL is a process in Data Warehousing and it stands for Extract, Transform and Load. It is a process in which an ETL tool extracts the data from various data source systems, transforms it in the staging area, and then finally, loads it into the Data Warehouse system.
13. How is ETL used in Data Migration Project? Explain it.
14. What is the Data Pipeline?
A data pipeline is a set of actions that ingest raw data from disparate sources and move the data to a destination for storage and analysis. A pipeline also may include filtering and features that provide resiliency against failure.
15. What is data purging?
Data purging is the process of permanently removing obsolete data from a specific storage location when it is no longer required.
16. What Is ODS (Operation Data Source)?
An ODS is a database designed to integrate data from multiple sources for additional operations on the data, for reporting, controls and operational decision support. Unlike a production master data store, the data is not passed back to operational systems.
17. What are the common ETL Testing scenarios?
The most common ETL testing scenarios are −
- Structure validation
- Validating Mapping document
- Validate Constraints
- Data Consistency check
- Data Completeness Validation
- Data Correctness Validation
- Data Transform validation
- Data Quality Validation
- Null Validation
- Duplicate Validation
- Date Validation check
- Full Data Validation using the minus query
- Other Test Scenarios
- Data Cleaning
18. What is partitioning in ETL?
Partitioning is when an area of data storage is sub-divided to improve performance. Think of it as an organizational tool. If all your collected data is in one large space without organization the digital tools used for analyzing it will have a more difficult time finding the information in order to analyze it. Partitioning your warehouse will create an organizational structure that will make locating and analyzing easier and faster.
19. Are you familiar with the Dynamic and the Static Cache?
When it comes to updating the master table, the dynamic cache can opt. Also, the users are free to use it for changing the dimensions. On the other side, the users can simply manage the flat files through the Static Cache. It is possible to deploy both the Dynamic and the Static Cache at the same time depending on the task and the overall complexity of the final outcome.
20. Can you tell me something about Bus Schema?
Dimension identification is something that is very important in the ETL and the same is largely handled by the Bus Schema.