Data Warehouse Appliances often ensure high data availability and fault tolerance by implementing a data replication strategy. This involves storing multiple copies of data or aggregations in different locations, which safeguards against data loss and system failure.

The phase of the evolution of data warehousing that involves gathering data from different sources and making it accessible in one place is known as "Data Integration." During this phase, data from diverse sources is collected, transformed, and loaded into the data warehouse to create a unified, accessible data repository for analytical purposes. Data integration is a crucial step in the data warehousing process.

Scaling out, also known as horizontal scaling, involves adding more machines or nodes to a system to handle increased load. It's a strategy used to improve a system's performance and capacity by distributing the workload across multiple resources.

In the ETL (Extract, Transform, Load) process, the first phase is "Extraction." This phase is responsible for gathering data from various sources, such as databases, flat files, and cloud sources, and extracting it for further processing and storage in a data warehouse.

Tableau is a widely recognized BI tool known for its capability to handle large datasets and create interactive dashboards. It offers a user-friendly interface for data visualization, making it a preferred choice for data professionals and analysts.

The "Extraction" phase in the ETL (Extract, Transform, Load) process involves retrieving data from various source systems, which may be databases, files, or other data repositories. This phase is the initial step in data warehousing, where data is collected from its sources for further processing and analysis.

In an in-memory data warehouse, the primary method to ensure data durability and prevent data loss is through the use of persistent data snapshots. These snapshots capture the in-memory data and save it to durable storage, providing a backup that can be used to recover data in case of system failure or data corruption.

The dimension table in a data warehouse provides context to the facts. It contains descriptive attributes and hierarchies that are used for filtering and grouping data in queries. This helps analysts and users understand the data in the fact table and answer various business questions.

For a startup concerned about upfront infrastructure costs and scalability, a cloud-based data warehouse is a suitable choice. Cloud solutions offer flexibility, scalability, and a pay-as-you-go model, reducing the initial investment. They can easily scale resources up or down as business needs evolve.

In OLAP (Online Analytical Processing) cubes, a dimension is defined by the combination of measures (such as sales, revenue), attributes (such as product names, customer names), and hierarchies (such as time periods). Dimensions are essential for structuring and analyzing data within an OLAP cube, providing a multi-dimensional view of the data.