While data timeliness, integrity, and consistency are common data quality dimensions, data diversity is not typically considered a primary dimension. Data diversity refers to the variety of data types, formats, and sources within a dataset, which may affect data integration and interoperability but is not a direct measure of data quality.

Denormalization in a physical data model is often employed to enhance query performance by reducing the need for joins and simplifying data retrieval, albeit at the potential cost of some redundancy.

Apache Spark utilizes HDFS (Hadoop Distributed File System) for fault-tolerant storage of Resilient Distributed Datasets (RDDs). HDFS provides the necessary durability and fault tolerance required for distributed processing in Spark.

When planning a migration to a Data Lake architecture, considerations should include scalability to handle large volumes of data, robust data governance practices to ensure data quality and compliance, stringent data security measures to protect sensitive information, and thoughtful data structure design to enable efficient data processing and analysis.

One drawback of using indexes is the potential for decreased performance due to the additional overhead incurred during data modification operations. This overhead can slow down insert, update, and delete operations.

Deduplication is a technique used to identify and remove duplicate records from a dataset. This process helps ensure data quality and accuracy by eliminating redundant information.

Conformed dimensions in Dimensional Modeling are dimensions that are consistent and shared across multiple data marts or data sets, ensuring uniformity and accuracy in reporting. Junk dimensions, on the other hand, contain miscellaneous or low-cardinality attributes that don't fit well into existing dimensions.

While an infinite retry mechanism may seem appealing for its potential to automatically resolve transient errors, it can introduce significant drawbacks. Delayed detection and resolution of underlying issues are major concerns. If the root cause of an error is not addressed promptly, it can lead to prolonged system instability and potential cascading failures. Additionally, an infinite retry mechanism can mask systemic problems, making it difficult to identify and address issues effectively.

HBase is a distributed, Key-Value database that runs on top of Hadoop. It provides real-time read/write access to large datasets, making it suitable for applications requiring low-latency data access.

CAP theorem primarily addresses the trade-offs between Consistency, Availability, and Partition tolerance in distributed systems, which are crucial considerations when designing and operating NoSQL databases.