Wipro ETL Tester Interview Questions, Process, and Tips

I have experience in identifying opportunities, building relationships, and driving growth in various sectors.

• Developed strategic partnerships that increased revenue by 30% in one year.

• Conducted market research to identify new business opportunities, leading to the launch of two successful products.

• Collaborated with cross-functional teams to enhance service offerings, resulting in improved customer satisfaction scores.

• ...read more

ETL stands for Extract, Transform, Load, a process for data integration and management in data warehousing.

• Extract: Collecting data from various sources like databases, APIs, or flat files. Example: Pulling sales data from a CRM system.

• Transform: Cleaning and converting data into a suitable format. Example: Changing date formats or aggregating sales figures.

• Load: Storing the transformed data into a target database or d...read more

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 t...read more

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 re...read more

DWH (Data Warehouse) is a centralized repository for integrated data, while Data Mart is a subset focused on specific business areas.

• DWH stores large volumes of historical data from multiple sources for analysis.

• Data Mart is designed for specific departments, like sales or finance.

• Example of DWH: A retail company's data warehouse containing sales, inventory, and customer data.

• Example of Data Mart: A sales data mart tha...read more

Primary keys uniquely identify records in a table, while foreign keys establish relationships between tables.

• A primary key is a unique identifier for a record in a database table.

• Example: In a 'Customers' table, 'CustomerID' can be a primary key.

• A foreign key is a field in one table that links to the primary key of another table.

• Example: In an 'Orders' table, 'CustomerID' can be a foreign key referencing 'Customers'.

• Pr...read more

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 ...read more

In my project, I perform validations such as data completeness, data accuracy, data transformation, and data integrity checks.

• Data completeness checks to ensure all required fields are populated

• Data accuracy checks to verify the correctness of data

• Data transformation checks to validate the transformation rules are applied correctly

• Data integrity checks to maintain data consistency and reliability

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 b...read more

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 dat...read more