Exploring Data Warehousing Concepts

What does ETL stand for in data warehousing?

• Extract, Transform, List

• Extract, Transfer, Load

• Extract, Transform, Load

• Extract, Transform, Link

Describe the main steps involved in the ETL process.

• Extract, Transfer, Load

• Extract, Transform, Link

• The main steps involved in the ETL process are Extract, Transform, and Load.

• Execute, Transform, Load

What is the purpose of data modeling in a data warehouse?

• The purpose of data modeling in a data warehouse is to organize and structure data for efficient analysis and reporting.

• To store data in a non-structured format.

• To eliminate the need for data analysis.

• To increase data redundancy in the warehouse.

Name two common data modeling techniques used in data warehousing.

• Star Schema and Snowflake Schema

• Diamond Schema

• Circle Schema

• Rectangle Schema

What is the difference between a star schema and a snowflake schema?

• A snowflake schema is always faster than a star schema.

• The main difference is that a star schema has denormalized dimension tables, while a snowflake schema has normalized dimension tables.

• A star schema has more tables than a snowflake schema.

• A star schema uses only fact tables without dimensions.

Explain the concept of data warehouse architecture.

• Data warehouse architecture only includes data storage without any processing layers.

• Data warehouse architecture includes layers such as data sources, staging area, data warehouse, and presentation layer for efficient data management and analysis.

• Data warehouse architecture does not involve any data sources or staging areas.

• Data warehouse architecture is solely focused on real-time data streaming.

What are the key components of a typical data warehouse architecture?

• Data sources, ETL processes, staging area, data warehouse, front-end tools

• Data mining techniques

• Data lakes

• Machine learning algorithms

Differentiate between OLAP and OLTP systems.

• OLAP systems are used for real-time transaction processing, while OLTP systems are for data warehousing.

• OLAP systems are for data analysis and reporting, while OLTP systems are for transaction processing and data management.

• OLAP systems are primarily used for online gaming, while OLTP systems are for e-commerce transactions.

• OLAP systems focus on data entry, while OLTP systems are designed for analytical queries.

What is the primary purpose of OLAP systems?

• To facilitate data entry and storage

• To manage real-time data processing

• To store large volumes of transactional data

• The primary purpose of OLAP systems is to support complex analytical queries and data analysis.

How does OLTP handle transaction processing?

• OLTP processes transactions using ACID properties to ensure reliability and efficiency.

• OLTP processes transactions without any reliability measures.

• OLTP uses eventual consistency for transaction processing.

• OLTP relies solely on user input for transaction validation.