Cognizant ETL Talend Developer Interview Questions and Answers

Schemas in Talend are used to define the structure of data being processed.

• Schemas define the columns and data types of input and output data in Talend jobs.

• They can be imported from databases, defined manually, or generated automatically.

• Schemas help ensure data quality and consistency in ETL processes.

To achieve rank login in Talend, you can use the tRank component.

• Use the tRank component in Talend to achieve rank login.

• Configure the tRank component to specify the input data and the column to rank on.

• The tRank component will assign a rank to each row based on the specified column.

• You can then use the ranked data for further processing or analysis.

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

Project architecture refers to the overall structure of a software project, including components, modules, and their interactions.

• Project architecture defines how different components of a software project are organized and interact with each other.

• It includes the high-level design of the system, such as the database structure, data flow, and integration points.

• Project architecture also involves decisions on technologi...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

Roles and responsibilities refer to the tasks and duties assigned to an individual within a specific job or position.

• Roles define the position or job title of an individual within an organization.

• Responsibilities outline the specific tasks, duties, and expectations associated with that role.

• Roles and responsibilities help clarify expectations, define boundaries, and ensure accountability.

• Examples include testing data e...read more

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. The

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;

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;

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 bus...read more