Which type of Slowly Changing Dimension (SCD) uses a separate table to store both current and historical data for an attribute?
- SCD Type 1
- SCD Type 2
- SCD Type 3
- SCD Type 4
SCD Type 2 is the type of Slowly Changing Dimension that uses a separate table to store both the current and historical data for an attribute. It allows you to maintain a historical record of changes over time while preserving the current value in the main table. This is particularly useful in data warehousing for tracking changes to dimension attributes.
In a star schema, if a dimension table contains a hierarchy of attributes (like Year > Quarter > Month), but these attributes are not broken into separate tables, this design is contrary to which schema?
- Fact Constellation Schema
- Galaxy Schema
- Hierarchical Schema
- Snowflake Schema
In a star schema, dimension tables are typically denormalized, meaning that hierarchies of attributes are not broken into separate tables. This design is contrary to the snowflake schema, where attributes are often normalized into separate tables to reduce redundancy. In a snowflake schema, the Year, Quarter, and Month attributes might be split into separate tables, leading to more complex joins.
What does the "in-memory" aspect of a data warehouse mean?
- Data is stored in RAM for faster access
- Data is stored on cloud servers
- Data storage on external storage devices
- Storing data in random memory locations
The "in-memory" aspect of a data warehouse means that data is stored in random-access memory (RAM) for faster access and processing. Storing data in RAM allows for high-speed data retrieval and analytics, as data can be accessed more quickly compared to traditional storage on external devices like hard drives. This leads to improved query performance and faster data analysis.
Which strategy involves splitting the data warehouse load process into smaller chunks to ensure availability during business hours?
- Data Compression
- Data Partitioning
- Data Replication
- Data Sharding
The strategy that involves splitting the data warehouse load process into smaller chunks to ensure availability during business hours is known as "Data Partitioning." Data is divided into partitions, making it more manageable and allowing specific segments to be loaded or accessed without disrupting the entire system. This is a common strategy for balancing data warehouse loads.
What potential issue arises when using a snowflake schema due to the normalization of dimension tables?
- Enhanced Data Integrity
- Improved Query Performance
- Increased Redundancy
- Simplified ETL Processes
Using a snowflake schema, which involves normalizing dimension tables, can lead to increased data redundancy. Normalization breaks down attributes into separate tables, which can result in more complex join operations, increased storage requirements, and potentially slower query performance due to the need for multiple joins.
Columnar databases are often favored in scenarios with heavy _______ operations due to their column-oriented storage.
- Aggregation
- Indexing
- Joining
- Sorting
Columnar databases are frequently preferred in scenarios with heavy aggregation operations. This is because their column-oriented storage allows for efficient processing of aggregation functions, making them well-suited for analytical and data warehousing workloads where aggregations are common.
What is a common reason for using a staging area in ETL processes?
- To reduce data storage costs
- To restrict access to the data warehouse
- To speed up the reporting process
- To store data temporarily for transformation and cleansing
A staging area in ETL processes is used to temporarily store data before it's transformed and loaded into the data warehouse. It allows for data validation, cleansing, and transformation without impacting the main data warehouse, ensuring data quality before final loading.
Which service provides fully managed, performance-tuned environments for cloud data warehousing?
- AWS EC2
- Amazon Redshift
- Azure SQL Database
- Google Cloud Platform
Amazon Redshift is a fully managed, performance-tuned data warehousing service provided by AWS. It is designed for analyzing large datasets and offers features like automatic backup, scaling, and optimization to ensure efficient data warehousing in the cloud.
In the context of data warehousing, what is the process of extracting, transforming, and loading data known as?
- Data Aggregation
- Data ETL
- Data Integration
- Data Mining
In data warehousing, the process of Extracting, Transforming, and Loading (ETL) data is crucial. ETL involves extracting data from source systems, transforming it to fit the data warehouse schema, and loading it into the data warehouse for analysis. It ensures data quality and consistency.
During which ETL phase might you apply data cleansing operations, such as removing duplicates or correcting data inconsistencies?
- Extraction
- Loading
- Reporting
- Transformation
Data cleansing operations, like removing duplicates and correcting data inconsistencies, are typically performed during the Transformation phase of the ETL process. This is when data is prepared for storage in the data warehouse and is where data quality improvements are made.