Massively Parallel Processing (MPP) architecture is known for its ability to scale out by distributing data, processing workloads, and query loads across multiple servers. This architecture enhances performance and allows data warehousing systems to handle large volumes of data and complex queries.

In-memory processing in Business Intelligence (BI) tools offers a significant advantage in terms of faster query performance. It stores data in system memory (RAM), allowing for quick data retrieval and analysis, which is crucial for real-time and interactive reporting. This speed improvement is a key benefit of in-memory processing.

Real-time data integration became a prominent feature in the Third Generation of data warehousing. During this era, there was a shift toward more real-time or near real-time data processing and integration, allowing organizations to make decisions based on the most up-to-date information.

In the context of Business Intelligence (BI), ETL stands for "Extract, Transform, Load." It refers to the process of extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse or BI system for analysis and reporting.

In ETL (Extract, Transform, Load) processes, data "transformation" primarily involves cleaning and restructuring the data. This phase ensures that data is in a suitable format for analysis and reporting, involving tasks like data cleansing, normalization, and data quality improvement.

Explanation:

When historical data has been corrupted, a point-in-time recovery is the most efficient method to restore the data to its state from one week ago. This approach allows you to specify a specific date and time to recover the data to, ensuring that the data reflects its state at that moment.

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.

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.

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.