In a star schema, the dimension tables are typically _______.
- Denormalized
- Hierarchically structured
- Highly normalized
- Loosely related
In a Star Schema, dimension tables are typically denormalized, meaning that redundant data is stored to improve query performance. Denormalization simplifies data retrieval as it reduces the need for joins between tables, which can be resource-intensive.
A financial institution is trying to optimize its data warehousing strategy. They are considering shifting from batch processing every night to more frequent updates throughout the day. What potential benefit might they achieve?
- Improved data freshness
- Increased data security
- Reduced storage costs
- Simplified data architecture
Shifting from batch processing to more frequent updates throughout the day can significantly improve data freshness. This means the data in the data warehouse is more up-to-date, which can lead to more accurate and timely decision-making. Real-time or near-real-time data can be crucial in financial institutions for risk management and customer service.
The process of transforming data from source systems into a format suitable for analysis in a data warehouse is known as _______.
- Data Aggregation
- Data Collection
- Data Integration
- Data Synchronization
The process of transforming data from source systems into a format suitable for analysis in a data warehouse is known as Data Integration. Data integration involves cleaning, transforming, and loading (ETL) data into the warehouse, ensuring its quality and compatibility.
In a traditional RDBMS, data is stored in _______ which are a collection of rows and columns.
- Graphs
- Indexes
- Sequences
- Tables
In a traditional Relational Database Management System (RDBMS), data is stored in tables, which are a structured collection of rows and columns. Each row represents a record, and each column represents a data attribute. Tables are used to organize and store data in a tabular format.
Which BI functionality allows users to ask and answer their own questions without involving IT to create a new report?
- Ad-hoc Reporting
- Data Extraction
- Database Maintenance
- ETL Processing
Ad-hoc reporting functionality enables users to create their own reports and answer specific questions without requiring IT involvement. It provides flexibility in querying and analyzing data as needed.
A company is setting up a new reporting database. They prioritize query speed over concerns about data redundancy. Which database design approach might they consider?
- Denormalization
- Entity-Relationship Modeling
- In-Memory Databases
- Normalization
When query speed is a priority and data redundancy concerns are secondary, a company might consider denormalization. This database design approach involves reducing the number of tables and increasing data redundancy to improve query performance.
Which architecture involves having multiple data marts sourcing from a central data warehouse?
- Data Lake Architecture
- Federated Data Warehouse
- Hub-and-Spoke Architecture
- Star Schema
The architecture that involves having multiple data marts sourcing from a central data warehouse is known as the Hub-and-Spoke architecture. In this setup, the central data warehouse serves as the primary repository, while data marts are created for specific business units or departments, allowing for a balance between centralized control and department-specific needs.
What is the primary purpose of a physical data model?
- Defining database table structures and access methods
- Documenting data lineage and metadata
- Identifying data integration opportunities
- Representing business concepts and relationships
The primary purpose of a physical data model is to define database table structures and access methods. It includes details such as table names, column data types, indexes, and keys. It serves as a blueprint for implementing the data design in a database management system.
In the context of Distributed Data Warehousing, what does "data locality" refer to?
- The geographical proximity of data across multiple data centers
- The logical organization of data within a database
- The number of data nodes in a cluster
- The physical location of data in a data center
"Data locality" in Distributed Data Warehousing refers to the geographical proximity of data across multiple data centers. This concept is essential for optimizing query performance, as it reduces data transfer latencies and speeds up data access when distributed data is physically closer to where it's needed.
A data engineer notices that the dimension tables in the data warehouse have become quite large and complex, with multiple levels of hierarchies. To improve the clarity and structure of the schema, which design modification should they consider?
- Create additional hierarchies
- Denormalize the dimensions
- Normalize the fact table
- Snowflake the dimensions
To improve the clarity and structure of dimension tables with multiple hierarchies, the data engineer should consider snowflaking the dimensions. Snowflaking involves breaking down complex dimensions into smaller, normalized tables to simplify queries and enhance maintainability.