For tracking changes in product prices over time, Type 2 Slowly Changing Dimensions (SCD) would be recommended. This type maintains a history of changes by creating new records for each change, preserving the old ones. It allows for accurate tracking of product price changes without altering existing records.

The connections between nodes in a graph database are called "Relationships." These relationships define the associations between different entities represented by the nodes. In a graph structure, relationships play a crucial role in establishing connections.

For financial transaction logs where data integrity is paramount, employing Lossless Compression techniques such as Delta Encoding or Block Compression is advisable. These methods reduce storage size without compromising data accuracy, ensuring compliance with regulatory requirements while managing limited storage effectively.

The GROUP BY clause in SQL is used to group rows based on the values in one or more columns. It is often used in conjunction with aggregate functions to perform operations on each group separately, providing a way to analyze and summarize data.

MySQL Workbench primarily offers graphical features for database design and management. Users can visually design, model, and manage databases, allowing for an intuitive and user-friendly approach to database development.

The notation (1,*) represents a Zero-to-Many cardinality, indicating that one entity must be present, but there can be many occurrences in the related entity. This is commonly used to represent optional relationships.

A key feature of ER diagram tools for beginners is a user-friendly drag-and-drop interface. This simplifies the process of designing ERDs by allowing users to easily add entities, attributes, and relationships to the diagram without the need for complex coding or scripting.

Implementing compression techniques in database systems poses challenges such as increased computational overhead. While compression reduces storage requirements, the process of compressing and decompressing data can be resource-intensive, impacting system performance. It's crucial to balance the benefits of compression with its computational costs.

Forward Engineering involves the generation of SQL scripts to create a database schema based on a conceptual model. This process translates the high-level design into a working database with tables, relationships, and constraints. SQL is commonly used for this purpose.

Challenges in Reverse Engineering include the potential loss of information, ambiguity in relationships, and inconsistencies in data types. Inferring a conceptual data model from an existing schema can be complex due to these factors, requiring careful analysis and interpretation.