![]() ![]() How would you decide whether to use a snowflake or star schema in a specific scenario? Insights into potential follow-up questions: Error handling: ETL tools enable error handling and exception management during the data integration. Data quality: ETL tools provide features for data cleansing, validation, and transformation, ensuring high data quality. Scalability: ETL tools can handle large volumes of data efficiently. Time-saving: ETL tools automate repetitive tasks, reducing the time required for data integration. The advantages of using an ETL tool include: ETL (Extract, Transform, Load) tools automate the process of extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse. What are the advantages of using an ETL tool?.There are different approaches to handling slowly changing dimensions, such as Type 1 (overwrite the existing value with the new value), Type 2 (add a new row with the new value and maintain history), and Type 3 (add additional columns to track the changes). ![]() Slowly changing dimensions refer to changes in dimensional attributes over time. How do you handle slowly changing dimensions?.It provides faster query performance but can result in data redundancy. On the other hand, a star schema is a simpler data modelling technique where dimension tables are denormalised into a single table. It helps in reducing redundancy and improving data integrity. A snowflake schema is a data modelling technique where dimension tables are normalised into multiple related tables. What is the difference between snowflake and star schemas?.Now let us dive into some common data warehouse interview questions and answers for both beginners and experts: Common Data Warehouse Interview Questions for Beginners ![]() Being well-prepared allows you to answer these questions and demonstrate your problem-solving skills confidently.
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