In big data analytics, the process of analyzing current and historical data to make predictions about future events is known as "Predictive Analytics." Predictive analytics uses statistical algorithms and machine learning techniques to identify patterns and trends in data, helping organizations make informed decisions and forecasts.

In Slowly Changing Dimension (SCD) Type 2, changes in dimension attributes are handled by marking the old record as inactive and inserting a new record with the updated data. This allows historical tracking of attribute changes.

"Data skewness" in data profiling refers to the tendency of data to be unbalanced or non-uniformly distributed. It indicates that the data has a skew or imbalance in its distribution, which can affect statistical analysis and modeling. Understanding skewness is crucial in data analysis and decision-making.

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 "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.

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.

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.

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.

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.

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.

To support in-depth sales data analysis with dimensions like region, product, and time, the best choice would be a Star Schema in the data warehousing architecture. OLAP Cubes are used to efficiently process complex queries and aggregations. Star Schema's simplicity and denormalized structure are well-suited for such analytical tasks.

Data Integration Tools are essential for combining data from various sources, such as databases, flat files, and cloud services, and loading it into the data warehouse. These tools handle data extraction, transformation, and loading (ETL) processes, ensuring data consistency and quality.