ETL Testing Interview Questions and Answers
ETL testing is an integral component of data integration and warehouse projects. Data engineers, analysts, and scientists must grasp its concepts and best practices for successful data testing projects.
Whether preparing for an interview or simply expanding your knowledge base, this SQL interview questions for ETL testing blog offers valuable insights and practical strategies for answering ETL testing scenario-based interview questions such as data validation, profiling, transformation integration and security.
ETL testing interview questions on SQL queries blog, we aim to equip you with the skills needed for ETL testing interview questions.
So, let’s dive in together and discover this together!
1. What is ETL testing?
ETL testing is moving data from a source system to a target system using ETL tools and ensuring the data is transferred correctly.
It involves testing various integration points such as data duplication, truncation, moving aggregated data, and reconciliation.
2. What are cubes in ETL testing?
Cubes are 3D views of the data that allow visualisation of data based on different permutations and combinations.
They represent multi-dimensional data and are essential for data analysis and reporting.
3. What are some challenges during the ETL process?
Challenges during the ETL process include data complexities from different source systems, timeline constraints for loading data into the data warehouse, data duplication, and the design of the source system and target data model.
4. What are the functions of reporting tools in ETL testing?
Reporting tools, including analysis and reporting services, help pre-aggregate data based on specific dimensions.
They are essential for generating reports and analysing data for different users.
5. What is the role of intermediate layers, such as staging layers, in ETL testing?
Intermediate layers, such as staging, move data into a uniform format or only carry some data from the source system.
They must be validated, and the target data warehouse is a separate test environment.
6. What are the various checks that must be performed during ETL testing?
During ETL testing, multiple checks must be performed, including data reconciliation, data cleaning, data quality checks, system security checks, data count checks, and reporting report testing.
7. What are the two types of data integration points?
Data duplication and truncation are the two integration points.
Data duplication occurs when the same data comes from multiple systems, while data truncation occurs when the data is defined differently in the target system.
8. What are the differences between OLTP and OLAP systems?
OLTP systems store data at a detailed level and are used for online transaction processing while reporting systems require aggregated data for analysis and reporting.
ETL is used to get the data from the transparent OLTP system and convert it into the desired format in the OLAP system.
9. What is the role of data validation in ETL testing?
Data validation is a part of ETL testing that involves validating the extracted data before transforming it into the target system.
It ensures that the data is accurate and consistent.
10. What is the purpose of transforming data during ETL testing?
Data transformation is a critical part of ETL testing that involves converting data from its original format into the desired format for analysis and reporting.
It may include cleaning, aggregating, or calculating data to meet the requirements of the target system.
11. What is the difference between a data warehouse and an OLAP system in terms of data?
A data warehouse is subject-oriented and contains data specific to the analysed subject.
At the same time, an OLAP system extracts and loads data from two subject-oriented OLAP systems into separate subject-oriented systems.
12. What are some issues with OLTP systems that ETL addresses?
ETL addresses issues with OLTP systems where data may not be in the desired format for the data warehousing system.
It homogenises data from different systems and performs equal processes on the data converted into the desired format to ensure data accuracy and consistency.
13. What is the role of ETL in data warehouses, and what does it involve?
ETL (Extraction, Transformation, Loading) is a crucial process in data warehouses that involves extracting, transforming, and loading data from various sources.
It changes the data format or performs transformations to convert data into a desired format for loading into a data warehouse.
14. What is the purpose of ETL in combining data from different systems?
ETL is necessary to consolidate data from different systems and ensure a centralised master data that can be accessed by both systems when there is no common platform for centralised data access.
15. What are the functions of N2 and business intelligence tools in ETL testing?
N2 and business intelligence tools help visualise and analyse the data moved during ETL testing.
They provide multi-dimensional views of the data, which can be used in reports for different users.
16. What are the three steps involved in the ETL process?
The ETL process involves three steps: extraction, where data is determined and selected from sources transmission, where data is transmitted from the source to the target system and loading, where data is loaded into the data warehouse and transformed into the desired format.
17. What is the role of harmonisation rules in the ETL process?
Harmonisation rules, such as cleansing or transformation rules, are used during the ETL process to harmonise data between systems to ensure data accuracy and consistency.
18. What determines the complexity or loading time of the ETL process?
The complexity or loading time of the ETL process depends on the type of source systems used, the number of sources and locations from which data is extracted, and the design of the source system and target data model.
19. What role does data extraction play in the ETL process, and how does it depend on the source system?
Data extraction is the first step in the ETL process. It involves determining the data needed, selecting the source system, deciding on extraction methods and transformation rules, and loading the data.
The extraction strategy depends on the source system, data storage, and the type of extraction job.
20. What is the difference in data extraction strategies for single and multiple-source systems?
For a single-source system, data extraction can be done directly.
However, specific extraction strategies are needed for multiple systems to extract data from each system at the right time.
21. Why is change data capture important in data extraction from old mainframe systems?
Change data capture is essential in old mainframe systems as they may not have a history of the data, making it difficult to maintain the history of a person.
Change data capture can be done by extracting everything from the source system and comparing it with what is already available.
22. What are the advantages and disadvantages of implementing change data capture in the source system versus extracting all data every time?
Implementing change data capture in the source system is an extra burden on the source system while extracting all data every time can burden the data warehouse.
23. What is the importance of scheduling data extraction jobs in real-life scenarios?
Data extraction jobs are typically scheduled during off-peak hours to capture data from the OLDB system and prepare the set for loading.
In real-life scenarios, extraction on production systems may not be possible at any time, so scheduling jobs is necessary to ensure the system’s performance.
24. What covers the maximum processing in the ETL process, and how is it done?
The maximum processing in the ETL process is done in three steps: extraction, transformation, and loading.
Extraction involves determining the data needed, selecting the source system, and deciding on extraction methods and transformation rules.
Transformation involves cleaning, formatting, and converting data into the desired format. Loading consists of loading the transformed data into the target system.
25. What is the definition of data transformation in the context of data processing?
Data transformation involves converting data from one format to another and bringing all data from disparate sources to a desired format.
It ensures that the data warehouse is up-to-date and ready for use.
26. Why is it essential to develop logic to change data types during transformations?
Logic must be developed to avoid losing data during data transformations to change data types from one format to another.
For example, fixing a data type in a database reserves memory.
At the same time, care is a shrinking and increasing data type that saves space based on the data type in the physical database.
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Data transformations are necessary in various scenarios where different data sources must be combined to achieve the desired granularity in the target system.
This includes harmonising data from multiple sources, converting data into a specific format, or aggregating data from different granularities.
28. What is an example of a common type of data transformation?
Bringing all codes of different data coding from other systems into one system is a common type of data transformation.
For example, we convert 1s and 0s to m and f for males and females.
29. What is an example of using external services for data transformations?
Using external services like Google’s geo database to complete data from a source system is an example of data transformations using external services.
30. What is the difference between data transformations and data validation?
Data transformations are the process of converting data from one format to another, while data validation is a type of transformation but not a transformation itself.
Data validation ensures that data meets specific criteria and is accurate.
31. What is an example of a parent-child relationship transformation?
Maintaining the integrity between tables when extracting data from an OLTP system is an example of a parent-child relationship transformation.
For instance, if a record cannot be loaded into a table, it must be able to be accessed if the depth record is not correct to maintain data integrity.
32. What are data transformations based on, and why are new columns necessary in an ETL process?
Data transformations can be based on the source data model, and new columns may be required to be added due to aggregated values or generated values that need to be included in the process.
33. What is an example of a generated value in a data transformation process?
An example of a generated value in a data transformation process is when generating a key for an incoming record, which involves getting the max value of the key in the dimension table and incrementing it to load the record into the dimension and ensure data integrity.
34. Why is homogenising data in a specific format during an ETL process essential?
Data transformations are performed to homogenise data in a specific format to ensure consistency and compatibility across different systems and geographies.
35. What are the two data loading methods in an ETL process, and which is more efficient for large OLTP systems?
The two data loading methods in an ETL process are reading everything and only deltas.
Reading everything is not always feasible for large OLTP systems, and the second method, which is more efficient, involves extracting and processing only incremental changes in the system.
36. How important is maintaining control tables during an ETL process?
Control tables store the status of operations in a data warehouse or ETL operations, such as successful or failed, and are used to run the next day’s job.
They help prevent data loss during the operation and handle data loading errors by notifying the operation team when a job fails.
37. What is the importance of data loading for maintaining data integrity and ensuring smooth operations?
Data loading is crucial for maintaining data integrity and smooth operations by ensuring data is accurately and efficiently transferred from its source to the destination system.
38. What was the solution proposed in the 1980s to address the issue of high data volumes impacting business transactions?
The solution proposed in the 1980s to address the issue of high data volumes impacting business transactions was to create separate databases for reporting and analysis.
39. What are OLTP and OLAP systems?
OLTP (Online Transaction Processing) is a database system that handles online transaction processing. At the same time, OLAP (Online Analytical Processing) is a system that allows for online analytical processing and stores historical data for analysis.
40. How does business intelligence help businesses understand customers’ needs and preferences?
Business intelligence helps businesses understand their customers’ needs and preferences by analysing customer data, enabling them to make informed decisions and improve their overall performance.
41. How can businesses utilise AI to understand customers’ needs and preferences?
Businesses can utilise AI to gain valuable insights into their customer’s needs and preferences by analysing large amounts of customer data and identifying patterns and trends that may not be apparent through manual analysis.
42. What are some linear sources from which data will be extracted and loaded into the data lake?
Data will be extracted and loaded from applications like SAP, Salesforce, file systems, Oracle, Microsoft SQL server, and DB2.
43. What is the ETL process, and what does it involve?
ETL (Extract, Transform, Load) is a process used to collect data from various sources, transform it into a usable format, and load it into the data warehouse.
44. What is batch processing, and how often does it run?
Batch processing is where data is extracted, transformed, and loaded into the data warehouse in batches. It runs periodically, with a schedule that can be dynamic for each job.
45. What is the role of Informatica Power Center in data integration?
Informatica Power Center is an ETL tool used for data integration. It integrates data from one place to another, ensuring the timing is accurate and efficient.
46. What tools are used to provide customers with easy-to-understand charts and reports?
Various tools like the tab loop from Salesforce, Microsoft Power BI, Click Tech, Looker, GCP, Google, and AWS provide easy-to-understand charts and reports to customers.
47. What machine learning and artificial intelligence algorithms are applied to the data lake?
Machine learning and artificial intelligence algorithms are applied to the data lake to improve user experiences.
48. What is the role of the data lake in Informatica’s system for analysing customer data?
The data lake is a large, massive data centre that stores data, including ingestion, tracking, storage, and data catalogue data bridge.
It handles data variables and prepares data for machine learning and artificial intelligence algorithms.
49. What is the ETL pipeline, and what role does machine learning play?
The ETL pipeline is a process used by Informatica to extract, transform, load, and apply machine learning algorithms to customer data.
Machine learning is an essential component of this pipeline.
50. What is the ETL process, and what are its main components?
The ETL process is a data integration process that involves extracting data from various sources, transforming it, and loading it into a target database or data warehouse. The main components of the ETL process are extraction, transformation, and loading.
51. What are the different layers of data in the ETL process?
The different layers of data in the ETL process include staging, intermediate, and data mark.
Data marks are subsets of data values, such as merchant, supplier, and finance data marks.
52. What are the primary skills required for an ETL tester?
An ETL tester should know business intelligence, data variables, data lakes, and ETL processes.
They should also have strong knowledge of SQL Server, Unix scripting, Python, and cloud technologies like AWS, Azure, No Flag, Hadoop, and One Spark.
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53. What is the role of ETL testers in the ETL process?
ETL testers validate and verify data during the ETL process to ensure it is not duplicated or lost.
They identify and prevent issues with data quality, preventing duplicates, data quality issues, and data loss.
54. What tools are commonly used in the ETL process?
Different ETL tools, such as Informatica Power Center, Intelligent Cloud Services, Microsoft, SSI, SQL Server Integration Service, and Oracle Data Integrated, play a crucial role in the ETL process.
Understanding these concepts is essential for successful interviews and projects.
55. What are the responsibilities of an ETL tester?
An ETL tester’s responsibilities include working with functional SMEs, reviewing requirements, mapping documents, converting mapping documents into SQL queries, supporting developers in the initial requirement-gathering stage, preparing test cases, and validating data.
They also need to know about ETL software and its components, such as creating design and executing test cases, test plans, and harnesses.
56. What is the role of an ETL tester in the testing process?
The ETL testing process involves identifying business requirements, validating data sources, designing test cases, executing test cases in different cycles, and providing summary reports to management and customers.
57. What are the two types of tables in a data warehouse, and what is their role?
The two types of tables in a data warehouse are dimension tables and fact tables. Dimension tables are master tables that contain primary keys, non-measurable attributes, and fact tables.
They store data related to products, employees, customers, locations, and other related data.
Fact tables collect data from the dimension table, which is used to analyse the performance of different areas.
58. What is the role of a primary key in a dimension table?
A primary key is the memory key for the attributes in a dimension table.
It is used to store the memory key and attributes, while non-measurable attributes are not measurable values.
Primary keys are essential as they help connect the dimension table to the fact table.
59. What is the agile framework concept, and how does an ETL tester need to be familiar with it?
The agile framework is a project management approach emphasising flexibility, collaboration, and customer satisfaction.
An ETL tester must be familiar with the elegant framework concept as they may work on projects that use this approach.
They must be able to work with various data sources and types and adapt to changing requirements.
60. What is the role of the dimension table in the snowflake schema?
The dimension table stores descriptive data about the facts in the fact table.
It helps analyse the data by providing attributes or characteristics about the data points.
61. What data is stored in the dimension table for sales transactions?
The dimension table stores data related to sales, such as the date, customer, employee, store, and region, along with their respective attributes or characteristics.
62. What is the purpose of the fact table in the snowflake schema?
The fact table stores the quantitative data or measures related to the business transactions. It aggregates the data from the dimension table and provides insights into the company’s performance.
63. What is the significance of the star schema in the snowflake schema?
The star schema is a simple and efficient data modelling technique that connects the fact table to the dimension tables using direct relationships.
It simplifies the querying process and improves data accessibility.
64. What data is stored in the employee table in the dimension table?
The employee table in the dimension table stores data related to employees, such as their ID, office addresses, and other relevant information.
65. What is the difference between data-based testing and ETL testing?
Data-based testing focuses on ensuring data accuracy in a database by comparing it to the data model, primary vital relationships, and metadata tables.
On the other hand, ETL testing compares data movement as expected, count, and business logic between the source and target data.
66. What is the role of normalisation in data management?
Normalisation is a technique to avoid data redundancy and eliminate duplicate data by breaking down larger tables into smaller, more manageable ones.
67. What is Query Surge, and how is it used in ETL testing?
Query Surgeis a tool used for ETL testing to compare data between the source and target data.
It extracts the data to a sheet for easy comparison, allowing for quick identification of any discrepancies.
68. What skills are necessary for professionals transitioning from data variables to data lakes?
ETL testing is crucial for professionals transitioning from data variables to data lakes.
It involves understanding the data pipeline and how it moves from source to target, leading to numerous job opportunities in the market.
69. What technologies are used in ETL testing, and what are their functions?
Technologies like Hive, Python, and Airflow are used in ETL testing. Hive is used for data processing, Airflow is a scheduler, and Python is a scripting automation tool.
70. What is involved in the ETL testing process?
The ETL testing process involves testing data from a heterogeneous source system to a data warehouse, which includes different tables.
In an OLTP system, the source system is the online transaction processing, while the target system is the data from different stages in the data warehouse.
The data is extracted and transformed using business logic and then loaded into the target table.
71. What is the difference between overwrite and no overwrite in ETL testing?
Overwrite is an option in which the source system has a large amount of data but only needs to fetch the last year or two.
If no overwrite query is written, the source will bring all records from the source.
72. What are the different levels of writing an ETL query?
ETL queries are at different levels, such as mapping and session level.
The mapping level query is written in the source, while the session level query is considered when an additional query is needed.
73. What does an ETL tool store?
ETL tools do not store data but metadata, which is transformed based on business logic and loaded into the target table.
74. What tools are commonly used for development in ETL (Extract, Transform, Load)?
Various tools are used for ETL development, such as Informatica, SAP BODS, Talend, IBM Data Stage, Oracle Data Integrator, and Microsoft SSIS.
75. How do developers ensure the data has been moved from source to target correctly?
Developers write code using tools like Informatica Power Center or SAP BODS, extract the source structure and store it in an RTFAT file using a DevOps tool like Jenkins, test the code in a centralised place, create a build number, and deploy the code in a QA environment.
76. How is testing done in ETL development?
Testing is done by executing the automatic workflow, loading data, and checking logs.
Simple testing is done for Oracle to Oracle, and transformation logic is used to load data into the target.
77. What is the role of Nexus in uniting a project?
Nexus creates the builder, which downloads the artefact into the environment.
The artefact is loaded into the target table structure in the testing environment, and the builder is then executed manually.
78. What is the importance of using the correct tool for loading data from developed environments?
The importance of using the correct tool for loading data from developed environments to ensure a clean and organised project.
79. What is the ideal testing process in the data migration process?
Ideal testing involves writing select queries when paying wire classes and then testing the database to ensure that the database is functioning correctly and the data is not truncated or incorrectly implemented.
80. How is the testing process divided?
The testing process is divided into different cycles based on the requirements and testing days needed.
It is further divided into ideal testing, where the database functions correctly, and testing in a controlled environment.
Common ETL testing SQL query interview questions and ETL testing interview questions and answers for experienced pdf include understanding its process flow, recognising data quality issues, outlining testing strategies for different data types, discussing tools and techniques for testing ETL systems, and more.
Prepare yourself for these SQL queries for ETL testing interview questions by developing an in-depth knowledge of data modelling, types, cleansing, transformation, and validation processes and ETL tools such as Talend, Informatica, or SQL Server Integration Services.
As part of your interview preparations, demonstrate your problem-solving capabilities by discussing how you would approach complex ETL scenarios involving managing data from multiple sources.
ETL testing interview questions for experienced processing transformations and maintaining security measures.
Be clear and assertive in your communication, as ETL testing questions require effective collaboration among data engineers, scientists and stakeholders.
Best of luck with your interview!
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