100+ ETL Tester Interview Questions and Answers

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

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

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

Query to fetch data greater than average, min and max salary.

• Calculate average, min, and max salary first

• Use a SELECT statement with WHERE clause to filter data greater than average, min, and max salary

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

Mapping design is the process of creating a mapping between source and target data structures.

• It involves identifying the source and target data structures

• Defining the transformation rules to convert the source data to target data

• Creating a mapping document to document the mapping rules

• Examples include ETL mapping design for data integration projects

Join is used to combine rows from two or more tables based on a related column, while union is used to combine the result sets of two or more SELECT statements.

• Join is used to retrieve data from multiple tables based on a related column

• Union is used to combine the result sets of two or more SELECT statements

• Join can be used with different types like INNER JOIN, LEFT JOIN, RIGHT JOIN, etc.

• Union removes duplicate rows from the combined result set

Use a dictionary to find duplicate values in an array of strings in Python.

• Create an empty dictionary to store the count of each string in the array.

• Iterate through the array and for each string, check if it exists in the dictionary. If it does, increment the count. If not, add it to the dictionary with a count of 1.

• After iterating through the array, check the dictionary for any strings with a count greater than 1. These are the duplicate values.

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

Types of SCD include Type 1, Type 2, Type 3, and Type 4.

• Type 1 - Overwrite: Old record is replaced with new data.

• Type 2 - Add new row: New record is added with a new surrogate key.

• Type 3 - Update: New column is added to track changes.

• Type 4 - Hybrid: Combination of Type 1 and Type 2.

Fact tables contain quantitative data while dimension tables contain descriptive attributes.

• Fact tables store measurable, quantitative data such as sales revenue or quantity sold

• Dimension tables store descriptive attributes related to the data in the fact table, such as product name or customer details

• Fact tables are typically normalized while dimension tables are denormalized for easier querying

• Example: In a sales database, the fact table would contain sales data while dimen...read more

Challenges in ETL testing include data quality issues, complex transformations, and handling large volumes of data.

• Data quality issues such as missing or incorrect data can impact testing results

• Complex transformations can be difficult to validate and may lead to errors in the ETL process

• Handling large volumes of data can slow down testing processes and require efficient testing strategies

A domain name from an email refers to the part after the @ symbol in an email address.

• The domain name is typically the name of the organization or service provider associated with the email address.

• For example, in the email address example@gmail.com, 'gmail.com' is the domain name.

SCD types refer to slowly changing dimensions in data warehousing. Star schema is denormalized while snowflake schema is normalized.

• SCD types refer to how dimensions change over time in a data warehouse

• Type 1: Overwrite existing data with new data

• Type 2: Keep track of historical data with new records

• Type 3: Keep track of some historical data with limited history

• Star schema is denormalized with a central fact table connected to dimension tables

• Snowflake schema is normalized wi...read more

Defect life cycle is the process of identifying, reporting, prioritizing, fixing, and verifying defects in software.

• Defect is identified by testers during testing

• Defect is reported to development team

• Development team prioritizes and fixes the defect

• Fixed defect is verified by testers

• If defect is not fixed, it goes back to development team

• If defect is fixed, it is closed

ETL stands for Extract, Transform, Load. It is a process of extracting data from various sources, transforming it into a consistent format, and loading it into a target database.

• Extract: Data is extracted from different sources such as databases, files, APIs, etc.

• Transform: Data is cleaned, validated, and transformed into a consistent format.

• Load: Transformed data is loaded into a target database or data warehouse.

• Stages of ETL: Extraction, Transformation, Loading.

Snowflake schema is a normalized form of star schema with additional dimension tables.

• Snowflake schema is a data modeling technique used in data warehousing.

• In snowflake schema, dimensions are normalized into multiple related tables.

• Snowflake schema reduces redundancy and improves data integrity.

• Star schema is a denormalized form of snowflake schema with a single dimension table.

• In star schema, dimensions are not normalized and are directly linked to the fact table.

• Star schem...read more

dense_rank() assigns a unique rank to each distinct row, row_number() assigns a unique sequential integer to each row.

• dense_rank() is used to assign a unique rank to each distinct row based on the specified order.

• row_number() is used to assign a unique sequential integer to each row in the result set.

• Example: dense_rank() - SELECT dense_rank() OVER (ORDER BY salary) AS rank FROM employees;

• Example: row_number() - SELECT row_number() OVER (ORDER BY hire_date) AS row_num FROM em...read more

Entry criteria and exit criteria are conditions that must be met before and after testing, respectively.

• Entry criteria are the conditions that must be fulfilled before testing can begin

• Exit criteria are the conditions that must be fulfilled before testing can be considered complete

• Entry criteria can include having test data ready, test environment set up, and test cases prepared

• Exit criteria can include all test cases executed, all defects fixed, and test reports generated

Scalar function is a function that returns a single value based on input parameters.

• Scalar functions are used in SQL to perform calculations on input values and return a single result.

• They can be used in SELECT statements, WHERE clauses, and other SQL queries.

• An example of a scalar function is the ABS() function, which returns the absolute value of a number.

Use the GROUP BY clause with COUNT() function to find duplicates in SQL.

• Use GROUP BY clause with COUNT() function to group the records by the columns you want to check for duplicates

• Filter the results by using HAVING clause to only show records with count greater than 1

• Example: SELECT column1, column2, COUNT(*) FROM table_name GROUP BY column1, column2 HAVING COUNT(*) > 1;

Slowly Changing Dimensions (SCD) are techniques used in data warehousing to track changes to dimension data over time.

• SCD Type 1: Overwrites old data with new data, losing historical information.

• SCD Type 2: Creates new records for each change, preserving historical data.

• SCD Type 3: Creates additional columns to track changes, allowing limited history.

• SCD Type 4: Uses separate tables to store historical and current data.

• Example: In a customer dimension, SCD Type 1 would update...read more

ETL tester tests in various stages to ensure data accuracy and completeness.

• Test extraction process to ensure correct data is pulled from source systems

• Test transformation process to ensure data is correctly transformed and formatted

• Test loading process to ensure data is loaded into target systems without errors

• Test data reconciliation to ensure data is complete and accurate in target systems

• Test error handling and logging to ensure errors are properly handled and logged

Constraints are rules or conditions that must be followed in order to ensure data integrity and accuracy.

• Constraints define the limits or restrictions on data values in a database table

• Examples of constraints include primary key, foreign key, unique key, and check constraints

• Constraints help maintain data quality and consistency in a database

Use SQL query with ORDER BY and LIMIT to get 4th highest row based on a column

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

• Use LIMIT 1 OFFSET 3 to get the 4th highest row

• Example: SELECT * FROM table_name ORDER BY column_name DESC LIMIT 1 OFFSET 3

Identify bottlenecks and inefficiencies in the code to optimize performance

• Analyze the code to identify any redundant or unnecessary operations

• Optimize data processing by using efficient algorithms and data structures

• Consider parallel processing or multi-threading for performance improvement

• Use profiling tools to identify performance bottlenecks and optimize them

• Implement caching mechanisms to reduce redundant computations

I prefer not to disclose my current salary as it is confidential.

• It is not appropriate to disclose current salary as it is confidential information.

• I am open to discussing a fair and competitive salary for the position.

• My focus is on finding a challenging role that aligns with my skills and experience.

• I am willing to negotiate based on the job responsibilities and market standards.

Yes

• I am flexible to work in any environment, be it a team or individual work

• I can adapt to different work cultures and work styles

• I am open to learning new technologies and tools

• I can work in different time zones and shifts if required

As an ETL Tester, a real-time example would be testing the data migration from a legacy system to a new system.

• Testing the accuracy of data migration from one system to another

• Ensuring data integrity during the migration process

• Validating the completeness of data after migration

• Verifying the consistency of data across systems

• Identifying and resolving any data quality issues

• Testing the performance of the ETL process

To read a parquet file, use a library like Apache Parquet or PyArrow to load the file and access the data.

• Use a library like Apache Parquet or PyArrow to read the parquet file

• Load the parquet file using the library's functions

• Access the data within the parquet file for analysis or processing

SCD stands for Slowly Changing Dimensions. There are three types of SCD: Type 1, Type 2, and Type 3.

• Type 1: Overwrites old data with new data.

• Type 2: Creates a new record for new data and keeps the old record for historical data.

• Type 3: Creates a new column for new data and keeps the old column for historical data.

Cross join combines each row from one table with each row from another table.

• Cross join produces a Cartesian product of the two tables involved.

• It does not require any common columns between the two tables.

• It can result in a large number of rows if the tables are big.

• Example: SELECT * FROM table1 CROSS JOIN table2;

Inner join combines rows from two tables based on a related column between them.

• Inner join returns only the rows that have matching values in both tables

• It is used to retrieve data that exists in both tables

• Example: SELECT Orders.OrderID, Customers.CustomerName FROM Orders INNER JOIN Customers ON Orders.CustomerID = Customers.CustomerID;

SCD types are used to track changes in data over time in a data warehouse.

• SCD stands for Slowly Changing Dimensions.

• There are three types of SCD: Type 1, Type 2, and Type 3.

• Type 1 overwrites old data with new data.

• Type 2 creates a new record for each change and keeps a history of changes.

• Type 3 keeps both old and new data in the same record.

• SCD types are important for maintaining data integrity and accuracy in a data warehouse.

Primary key is a unique identifier for each record in a database table.

• Primary key ensures each record in a table is unique

• It can be a single column or a combination of columns

• Primary key values cannot be NULL

Use SQL query with ORDER BY and LIMIT to fetch nth highest salary

• Use ORDER BY salary DESC to sort salaries in descending order

• Use LIMIT n-1,1 to fetch the nth highest salary

• Replace n with the desired position of the salary

Data acquisition is the process of collecting, gathering, and capturing data from various sources.

• Data acquisition involves retrieving data from different sources such as databases, files, APIs, or sensors.

• It is an essential step in the ETL (Extract, Transform, Load) process.

• Data acquisition can be manual or automated, depending on the source and the system being used.

• Examples of data acquisition include extracting customer information from a CRM system, importing data from a...read more

Data reconciliation is the process of comparing and verifying data between two or more sources to ensure accuracy and consistency.

• Data reconciliation involves comparing data from different sources, such as databases or files.

• It aims to identify any discrepancies or inconsistencies in the data.

• Reconciliation can be done by comparing data values, counts, or summaries.

• Examples include comparing sales data between a point-of-sale system and an inventory database.

• Data reconciliati...read more

Scrum ceremonies are regular meetings held in Agile development to facilitate collaboration, planning, and review.

• Scrum ceremonies are time-boxed events that occur at specific intervals during a sprint.

• The ceremonies include Sprint Planning, Daily Stand-up, Sprint Review, and Sprint Retrospective.

• Sprint Planning involves determining the work to be done in the upcoming sprint.

• Daily Stand-up is a brief meeting where team members discuss progress, challenges, and plans for the d...read more

Use SQL query with subquery to find second highest salary.

• Use ORDER BY and LIMIT to get the second highest salary.

• Use a subquery to exclude the highest salary from the result set.

• Example: SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees);

Union, Union All, and Difference are SQL set operators used to combine or compare data from multiple tables.

• Union combines the results of two or more SELECT statements and removes duplicates.

• Union All combines the results of two or more SELECT statements and includes duplicates.

• Difference (also known as Except) returns only the rows from the first SELECT statement that are not in the second SELECT statement.

• All three operators require that the SELECT statements have the same ...read more

SQL query to retrieve even and odd numbers

• Use the modulo operator (%) to determine if a number is even or odd

• For even numbers: SELECT number FROM table WHERE number % 2 = 0

• For odd numbers: SELECT number FROM table WHERE number % 2 <> 0

SQL query to find the second highest salary in a table.

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

• Use the LIMIT clause to retrieve the second row after skipping the first row.

Use SQL query with ORDER BY and LIMIT to get 3rd highest salary.

• Use SELECT statement with ORDER BY salary DESC to sort salaries in descending order

• Use LIMIT 1,1 to skip the first two highest salaries and get the third highest salary

Customers use data for various purposes such as analysis, decision-making, reporting, and personalization.

• Data is used for analysis to gain insights and identify patterns or trends.

• Data helps in decision-making by providing information and supporting evidence.

• Data is used for reporting to communicate information and track performance.

• Data enables personalization by tailoring products or services based on customer preferences.

• Examples: analyzing sales data to identify top-sell...read more