80+ ETL Developer Interview Questions and Answers

APT_CONFIG_FILE is a configuration file used by Advanced Package Tool (APT) to manage packages in Linux.

• APT_CONFIG_FILE contains settings for APT such as proxy settings, package sources, and authentication credentials.

• Partition refers to dividing a hard drive into separate sections for different purposes.

• APT_CONFIG_FILE can be used to specify package sources for specific partitions.

• For example, if a user has a separate partition for development packages, they can specify the ...read more

SCD Type 2 is used to track historical changes in data by creating new records for changes.

• Identify changes in source data

• Insert new record with updated data

• Update end date of previous record

• Add post commands like updating flags or triggers

• Example: If a customer changes their address, a new record is created with the updated address while the previous record is marked as expired.

Using PIVOT in SQL to transform input table into expected output

• Use the PIVOT keyword followed by the aggregation function and column to pivot on

• Specify the values to pivot on as columns in the output table

• Include the FOR clause to specify the values to pivot on

• Example: SELECT * FROM input_table PIVOT (SUM(value) FOR category IN ('A', 'B', 'C')) AS output_table

A trigger is a special type of stored procedure that automatically executes in response to certain events.

• Triggers are used to enforce business rules or to perform complex calculations.

• There are two types of triggers: DML triggers and DDL triggers.

• DML triggers fire in response to DML events (INSERT, UPDATE, DELETE).

• DDL triggers fire in response to DDL events (CREATE, ALTER, DROP).

• Triggers can be used to audit changes to data, enforce referential integrity, or perform custom v...read more

RCP stands for Rich Client Platform. It is a framework used for developing desktop applications.

• RCP is based on Eclipse platform and provides a set of reusable components for building desktop applications.

• It provides a modular architecture that allows developers to easily add or remove features.

• RCP applications can be customized using plug-ins and extensions.

• I have used RCP in a project where we needed to develop a desktop application for data analysis and visualization.

• We us...read more

Dimension tables store descriptive attributes while fact tables store quantitative data. Dimension tables are loaded first.

• Dimension tables contain attributes like customer name, product category, etc.

• Fact tables contain quantitative data like sales revenue, quantity sold, etc.

• Dimension tables are typically loaded first as they provide context for the quantitative data in fact tables.

Aggregator stage methods include count, sum, average, min, max, first, last, and concatenate.

• Count: counts the number of input rows

• Sum: calculates the sum of a specified column

• Average: calculates the average of a specified column

• Min: finds the minimum value of a specified column

• Max: finds the maximum value of a specified column

• First: returns the first row of the input

• Last: returns the last row of the input

• Concatenate: concatenates the values of a specified column

Dimensional modeling factors include business requirements, data granularity, and data integration.

• Identify business requirements for the data warehouse

• Determine the level of data granularity needed for analysis

• Ensure data integration across multiple sources

• Choose appropriate dimensions and hierarchies

• Consider performance and scalability

• Examples: time, geography, product, customer

The prerequisites to use sort method in aggregator stage.

• The input data must be sorted by the same key as the one used in the aggregator stage.

• The input data must be partitioned by the same key as the one used in the aggregator stage.

• The sort method should be used before the aggregator stage in the data flow.

• The sort method should be used only if the data is too large to fit in memory.

• The sort method can be used with both ascending and descending order.

I approach data modeling by understanding requirements, defining entities, and ensuring data integrity and scalability.

• Understand business requirements: Engage with stakeholders to gather detailed requirements.

• Identify entities and relationships: Determine key entities (e.g., customers, orders) and their relationships (e.g., one-to-many).

• Choose the right model: Decide between star schema, snowflake schema, or normalized models based on use case.

• Define attributes: Specify attr...read more

There are several types of join operations in SQL, each serving different purposes for combining data from multiple tables.

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

• LEFT JOIN (or LEFT OUTER 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 (or RIGHT OUTER JOIN): Returns all records from the right tabl...read more

Data modeling involves structuring data into Fact and Dimension tables for efficient analysis and reporting.

• Identified business requirements to determine key metrics and dimensions.

• Created a star schema with a central Fact table (e.g., Sales) linked to Dimension tables (e.g., Customer, Product).

• Fact tables store quantitative data (e.g., sales amount, quantity sold) while Dimension tables provide context (e.g., customer demographics).

• Used tools like ER diagrams to visualize re...read more

Query optimization involves steps to improve the performance of database queries.

• Identify slow queries using tools like query logs or profiling.

• Analyze query execution plans to understand how queries are being processed.

• Optimize queries by adding indexes, rewriting queries, or restructuring data.

• Consider factors like data distribution, join types, and query complexity.

• Test and benchmark optimized queries to ensure performance improvements.

Normalization is a database design technique to reduce data redundancy and improve data integrity.

• 1NF (First Normal Form): Ensures that all columns contain atomic values. Example: A table with a 'Phone Numbers' column should have separate rows for each number.

• 2NF (Second Normal Form): Achieves 1NF and removes partial dependencies. Example: If a table has 'StudentID' and 'CourseID', the course name should be in a separate table.

• 3NF (Third Normal Form): Achieves 2NF and removes...read more

I have never crossed project deadlines in my career as an ETL Developer.

• I have always been proactive in managing my time and resources to ensure timely completion of projects.

• I prioritize tasks and create a detailed project plan to track progress and meet deadlines.

• I communicate effectively with stakeholders to manage expectations and address any potential delays.

• I have successfully completed multiple projects within the given timelines.

• I continuously monitor project progress...read more

Effective data loading strategies ensure data integrity and performance in ETL processes.

• Use incremental loading to minimize data transfer, e.g., only load new or updated records.

• Implement error handling to manage data quality issues, such as logging errors for review.

• Optimize batch sizes for loading to balance performance and resource usage, e.g., load in batches of 1000 records.

• Consider using staging tables to validate data before final loading into production tables.

• Monito...read more

SCD Type 2 is used to maintain historical data in a dimension table.

• SCD Type 2 is used to track changes in dimension attributes over time.

• It creates a new record for each change and maintains a history of all changes.

• It includes additional columns like start date, end date, and version number.

• Example: Employee table with changes in job title, salary, and department over time.

• It helps in analyzing trends and making informed decisions based on historical data.

Cluster index physically orders the data on disk, while non-cluster index does not.

• Cluster index physically orders the data on disk based on the indexed column

• Non-cluster index does not physically order the data on disk

• Cluster index can only have one per table, while non-cluster index can have multiple

• Cluster index is faster for retrieval but slower for inserts and updates

Excel formulas can be used to calculate statistics and analyze data.

• Use SUM function to calculate total sum of a range of cells.

• Use AVERAGE function to find the average of a range of cells.

• Use COUNT function to count the number of cells that contain numbers in a range.

• Use MAX and MIN functions to find the maximum and minimum values in a range.

• Use IF function to perform conditional calculations based on certain criteria.

Python function to process data

• Define a function that takes in the data as input

• Process the data using Python code

• Return the processed data

Yes, I have created a complex SSIS package for loading data from multiple sources into a data warehouse.

• The SSIS package involved extracting data from various sources such as SQL Server, Excel files, and flat files.

• I used data flow tasks, conditional split transformations, and lookup transformations to cleanse and transform the data before loading it into the data warehouse.

• I implemented error handling and logging mechanisms to ensure the reliability and traceability of the d...read more

Informatica Powercentre is an on-premise ETL tool, while iics is a cloud-based ETL tool.

• Informatica Powercentre is an on-premise ETL tool, meaning it is installed and run on the user's own hardware and infrastructure.

• iics (Informatica Intelligent Cloud Services) is a cloud-based ETL tool, allowing users to access and use the tool via the internet.

• Informatica Powercentre requires manual upgrades and maintenance, while iics is automatically updated by Informatica.

• Informatica Po...read more

Handling late arriving dimensional data involves strategies to ensure data integrity and accuracy in ETL processes.

• Implement a staging area to temporarily hold late arriving data before integration.

• Use surrogate keys to maintain historical accuracy when late data arrives.

• Apply business rules to determine how to handle late data, such as updating existing records or creating new ones.

• Example: If a new customer record arrives late, update the existing customer dimension with th...read more

SQL Server has several isolation levels that control transaction visibility and concurrency. The default is Read Committed.

• Read Uncommitted: Allows dirty reads. Example: SELECT * FROM Orders WITH (NOLOCK).

• Read Committed: Default level. Prevents dirty reads. Example: SELECT * FROM Orders.

• Repeatable Read: Prevents non-repeatable reads. Locks rows read during the transaction.

• Serializable: Highest level. Prevents phantom reads by locking the entire range of rows.

• Snapshot: Provide...read more

Type 2 SCD is used to track historical changes in data, creating new records for each change.

• In our project, we use Type 2 SCD to track changes in customer information such as address, phone number, and email.

• When a customer's address changes, a new record is created with the updated address and a new surrogate key.

• This allows us to maintain a history of customer information and analyze trends over time.

Pragma is used to provide instructions to the compiler, temporary table is a table that exists temporarily, SP is a stored procedure, cursor is used to iterate through a result set, finding second highest salary involves sorting the salaries in descending order and selecting the second row.

• Pragma is used to provide instructions to the compiler

• Temporary table is a table that exists temporarily

• SP stands for stored procedure

• Cursor is used to iterate through a result set

• To find t...read more

Yes, I am open to taking on a testing project as part of my role as an ETL Developer.

• I have experience with testing tools and methodologies in my previous roles.

• I understand the importance of testing in ensuring data accuracy and quality.

• Taking on a testing project will help me improve my skills and knowledge in ETL development.