100+ ETL Tester Interview Questions and Answers

Star schema has a single fact table and denormalized dimension tables, while snowflake schema has normalized dimension tables.

• Star schema is simpler and easier to understand, but can lead to data redundancy.

• Snowflake schema is more complex but saves storage space and allows for more flexibility in querying.

• Star schema is better suited for smaller datasets, while snowflake schema is better for larger datasets.

• Example of star schema: sales fact table with denormalized customer ...read more

Reorder a table to place 'India' at the top, followed by other countries.

• Use SQL to reorder the results: SELECT country FROM table ORDER BY CASE WHEN country = 'India' THEN 0 ELSE 1 END;

• This SQL query prioritizes 'India' by assigning it the lowest sort value.

• The rest of the countries will follow in their natural alphabetical order.

ETL architecture refers to the design and structure of the ETL process.

• ETL architecture involves three main components: extraction, transformation, and loading.

• Data is extracted from various sources, transformed to fit the target system, and loaded into the target database.

• ETL architecture can be implemented using different tools and technologies, such as ETL software, data integration platforms, and cloud-based solutions.

• The architecture should be designed to ensure data acc...read more

A data repository is a centralized location where data is stored, managed, and maintained.

• It is used to store and manage data in a structured manner

• It can be a database, data warehouse, or data lake

• It allows for easy access and retrieval of data

• Examples include Hadoop Distributed File System (HDFS), Amazon S3, and Oracle Database

Use OFFSET and FETCH in SQL to retrieve specific records from a table.

• Use OFFSET to skip the first n-1 records

• Use FETCH to limit the number of records to be retrieved

• Example: SELECT * FROM table_name OFFSET 4 ROWS FETCH NEXT 5 ROWS ONLY

Drop command deletes the table structure along with data, while truncate command deletes only the data.

• Drop command removes the table structure and all its data, while truncate command only removes the data but keeps the table structure intact.

• Drop command is a DDL (Data Definition Language) command, while truncate command is a DML (Data Manipulation Language) command.

• Drop command is slower than truncate command as it involves deleting the table structure.

• Example: DROP TABLE ...read more

Difference between where and having clause and severity and priority

• Where clause is used to filter rows before grouping while having clause is used to filter groups after grouping

• Severity is the degree of impact of a defect on the system while priority is the order in which defects should be fixed

• Where clause is used with SELECT, UPDATE and DELETE statements while having clause is used with GROUP BY clause

• Severity is usually assigned by the tester while priority is assigned b...read more

A data mart is a subset of a larger data warehouse that is designed to serve a specific business unit or department.

• Contains a subset of data from a larger data warehouse

• Designed to serve a specific business unit or department

• Provides a more focused view of data for analysis and reporting

• Can be created using a top-down or bottom-up approach

• Examples include sales data mart, marketing data mart, finance data mart

Joins are SQL operations that combine rows from two or more tables based on related columns.

• INNER JOIN: Returns records with matching values in both tables. Example: SELECT * FROM A INNER JOIN B ON A.id = B.id;

• LEFT JOIN: Returns all records from the left table and matched records from the right table. Example: SELECT * FROM A LEFT JOIN B ON A.id = B.id;

• RIGHT JOIN: Returns all records from the right table and matched records from the left table. Example: SELECT * FROM A RIGHT ...read more

Iterative model is a software development model where the development process is divided into smaller iterations.

• Iterative model involves repeating the development process in small cycles.

• Each cycle involves planning, designing, building, testing and evaluating the software.

• Feedback from each cycle is used to improve the software in the next cycle.

• Iterative model is useful when requirements are not clear or keep changing.

• Examples of iterative models include Agile and Scrum.

Use SQL query to find the nth row and top 50% records using string function

• To find the nth row, use LIMIT and OFFSET in SQL query

• To find top 50% records, use PERCENTILE_CONT function in SQL

• String functions like SUBSTRING can be used to manipulate string data

The interview questions are related to SQL and data manipulation.

• To fetch the last record of a column, you can use the ORDER BY clause with DESC keyword and LIMIT 1.

• Dense rank and rank are both ranking functions in SQL, but dense rank assigns consecutive rank numbers to rows, while rank leaves gaps in the ranking sequence.

• To fetch the end of 5 characters from a string column, you can use the RIGHT function in SQL.

The schema used in my project was a star schema.

• Star schema is a type of data warehouse schema where a central fact table is connected to multiple dimension tables.

• The fact table contains the measurements or metrics of the business process, while the dimension tables provide context and descriptive attributes.

• This schema is commonly used in data warehousing and business intelligence applications.

• Example: In a sales analysis project, the fact table could contain sales transact...read more

Star Schema is a type of database schema where a central fact table is connected to multiple dimension tables. Snowflake Schema is a normalized form of Star Schema where dimension tables are further normalized into sub-dimension tables.

• Star Schema has a central fact table connected to multiple dimension tables

• Snowflake Schema is a normalized form of Star Schema with sub-dimension tables

• Star Schema is denormalized for better query performance

• Snowflake Schema is normalized for ...read more

Query for last hour transaction details through net banking or card

• Use the transaction table to filter transactions by timestamp

• Join with the net banking or card table to get additional details

• Filter for transactions within the last hour using a WHERE clause

• Order the results by timestamp to get the most recent transactions first

A fact is a piece of information that is known to be true or proven through evidence.

• Facts are objective and can be verified through research or observation.

• Facts are not opinions or beliefs.

• Examples of facts include the boiling point of water, the population of a city, and historical events that have been documented.

• Facts can be used to support arguments or conclusions.

• Facts can change over time as new information is discovered or theories are revised.

Star schema has a single fact table connected to multiple dimension tables directly, while snowflake schema has normalized dimension tables connected to each other.

• Star schema has denormalized dimension tables, making it easier to query but potentially less efficient for storage.

• Snowflake schema has normalized dimension tables, reducing redundancy but potentially making queries more complex.

• Star schema is more suitable for data warehousing applications with simpler queries, w...read more

Group by clause is used in SQL to group rows that have the same values into summary rows.

• Group by clause is used with aggregate functions like SUM, COUNT, AVG, etc.

• It is used to group rows based on one or more columns specified in the query.

• Group by clause must be used after the WHERE clause and before the ORDER BY clause.

• Example: SELECT department, COUNT(*) FROM employees GROUP BY department;

Different types of data validation and checks in ETL testing include schema validation, data completeness checks, data transformation checks, and data quality checks.

• Schema validation ensures that the data conforms to the expected structure and format.

• Data completeness checks verify that all expected data is present and accounted for.

• Data transformation checks ensure that data is accurately transformed according to business rules.

• Data quality checks assess the accuracy, consi...read more

SQL query to find second highest salary.

• Use ORDER BY clause in descending order to sort the salaries

• Use LIMIT 1,1 to select the second highest salary

• Example: SELECT salary FROM employees ORDER BY salary DESC LIMIT 1,1

Delete removes specific records from a table; drop removes the entire table structure and its data.

• DELETE is a DML command used to remove specific rows from a table based on a condition.

• Example: DELETE FROM Employees WHERE EmployeeID = 1; removes the employee with ID 1.

• DROP is a DDL command that removes the entire table structure and all its data permanently.

• Example: DROP TABLE Employees; deletes the entire Employees table.

• DELETE can be rolled back if used within a transactio...read more

Defects life cycle is a process that describes the stages of a defect from discovery to resolution.

• Defect is identified and reported

• Defect is assigned to a developer

• Developer fixes the defect

• Defect is retested by the tester

• If the defect is still present, it is reopened

• If the defect is fixed, it is closed

Test data creation types include manual, automated, random, boundary, and negative testing.

• Manual testing involves creating data by hand

• Automated testing uses tools to generate data

• Random testing involves creating data randomly

• Boundary testing involves testing data at the limits of its range

• Negative testing involves testing invalid or unexpected data

To test a full load vs incremental load, compare the results of loading all data at once vs loading only new or updated data.

• Create test cases to verify the accuracy of data loaded during a full load.

• Create test cases to verify that only new or updated data is loaded during an incremental load.

• Compare the results of the full load and incremental load to ensure consistency and accuracy.

• Verify that the data integrity is maintained during both types of loads.

• Use tools like SQL q...read more

I performed various types of data validation including format validation, completeness validation, and referential integrity validation.

• Format validation: Ensuring that the data is in the correct format as per the defined specifications. For example, validating that a date field is in the format 'YYYY-MM-DD'.

• Completeness validation: Checking if all the required fields are present and populated in the data. For example, ensuring that a customer record has a name, address, and ...read more

The recent project flow involved data extraction, transformation, and loading using ETL tools.

• The project started with identifying the data sources and requirements.

• Then, the data was extracted from various sources using ETL tools like Informatica, Talend, etc.

• The extracted data was transformed as per the business rules and requirements.

• Finally, the transformed data was loaded into the target system.

• The project also involved testing the ETL process and ensuring data accuracy ...read more

Use SQL query with GROUP BY and HAVING clause to delete duplicates from each department

• Use GROUP BY clause to group the records by department

• Use HAVING clause to filter out the duplicates within each department

• Write a DELETE statement to remove the duplicate records