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.

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 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.

To ensure quick recovery without data inconsistency in case of an ETL process failure, the retail company should consider using checkpoints and logging. Checkpoints allow the process to save its progress at various stages, and logging records all activities and changes. In case of failure, the process can resume from the last successful checkpoint, minimizing data inconsistencies and potential data loss.

In the context of dashboards, a "Tile" is used to describe a graphical representation that provides at-a-glance views of key performance indicators (KPIs). Tiles are often customizable components that display summarized data or metrics, making it easy for users to monitor and understand essential information.

The main advantage of distributing data across multiple storage devices or locations in a Distributed Data Warehousing setup is scalability and load balancing. It allows for the efficient distribution of data, ensuring that query workloads can be evenly spread across resources, thus optimizing performance and handling increased data volumes effectively.