Incremental extraction involves extracting only the new or updated data since the last extraction, reducing processing time and resource usage compared to extracting all data every time.

In a Data Lake, raw data is stored in its raw form, without any processing or transformation. This allows for flexibility in data analysis and exploration, as the data retains its original structure.

Slowly Changing Dimensions (SCDs) in data warehousing refer to dimensions that change slowly over time, requiring special handling to track historical changes accurately. Common SCD types include Type 1 (overwrite), Type 2 (add new row), and Type 3 (add new column).

Employing Conformed Dimensions in Dimensional Modeling would ensure consistency and compatibility across various parts of the data warehouse, enabling effective analysis of patient demographics, diagnoses, and treatments.

Apache Kafka would be an ideal choice for communication in a distributed system due to its ability to handle large volumes of data streams efficiently and its fault-tolerant nature. Kafka's distributed architecture ensures high scalability and reliability, making it suitable for real-time data processing and communication between nodes in a distributed environment. Unlike HTTP, TCP/IP, and UDP, Kafka is specifically designed for distributed messaging and can support various communication patterns such as publish-subscribe and message queuing.

Apache Airflow offers a rich ecosystem of integrations and plugins for extending its functionality and integrating with various technologies. Common integrations include Apache Spark for distributed data processing, Kubernetes for container orchestration, and cloud platforms like AWS and GCP for seamless integration with cloud services. These integrations enable users to leverage existing tools and platforms within their Airflow workflows, enhancing flexibility and scalability.

Data modeling best practices emphasize the importance of maintaining consistency between different levels of data models to ensure that changes or updates are accurately reflected across the entire model hierarchy.

Data Missingness refers to the proportion of missing values in a dataset. It indicates the extent to which data points are absent or not recorded for certain variables. Understanding data missingness is crucial for data analysis and modeling as it can affect the validity and reliability of results. Techniques such as data imputation may be used to handle missing data effectively.

The main difference between DataFrame and RDD in Apache Spark lies in their approach to data processing. DataFrames offer structured data processing capabilities, while RDDs handle unstructured data and provide more low-level control.

The physical data model includes details such as data types, indexes, and storage specifications, which are essential for designing the underlying database structure and optimizing performance and storage.