Eager loading is a technique where related data is loaded along with the main entity. It reduces the number of database queries by loading all required data in one go, thus improving performance. Lazy loading, on the other hand, defers the loading of related data until it is explicitly accessed, which may lead to additional database calls. Explicit loading is used when you need to load related entities explicitly. Raw SQL queries bypass the Entity Framework and directly query the database, which might be less efficient in some cases. Eager loading is generally recommended for improving query performance.

Managing the lifetime of the DbContext is crucial in scalable Entity Framework applications to ensure data consistency. DbContext maintains a connection to the database and tracks changes made to entities. If the DbContext is not managed properly, it can lead to issues such as memory leaks, performance degradation, and data inconsistencies. Proper management ensures that the DbContext is disposed of when it's no longer needed, freeing up resources and preventing potential issues.

Batching of operations in Entity Framework reduces the number of database round trips by combining multiple operations (such as inserts, updates, or deletes) into a single batch. This reduces network overhead and can lead to significant performance improvements, especially in scenarios involving bulk data operations.

Using parameterized queries in Entity Framework is crucial for scalability because it helps prevent SQL injection attacks by separating SQL code from user input. This practice reduces the risk of malicious SQL injection attacks and improves the security of the application.

Using complex LINQ queries in scalable Entity Framework applications can potentially degrade scalability due to increased processing overhead. Complex queries often involve intricate logic and may require significant computational resources, leading to slower performance and reduced scalability, especially under high loads. It's essential to carefully design and optimize queries to minimize complexity and ensure efficient execution, thus maintaining scalability in Entity Framework applications.

The use of explicit loading in Entity Framework can enhance scalability by enabling on-demand loading of related entities. This approach allows selective loading of data as needed, reducing the overall data transfer and improving performance, especially in scenarios with large datasets or complex relationships. In contrast, lazy loading may lead to performance degradation by loading all related entities upfront, increasing resource consumption and potentially impacting scalability negatively. Leveraging explicit loading provides finer control over data retrieval, optimizing scalability in Entity Framework applications.

Splitting DbContext into multiple smaller DbContexts allows for better organization and management of entities, which can enhance scalability by reducing the complexity and size of each individual context. This approach also helps in optimizing database interactions and improves performance by minimizing the overhead associated with tracking large numbers of entities.

Batching refers to combining multiple database operations into a single batch before sending it to the database server. This can significantly reduce the overhead associated with executing individual database commands, particularly in scenarios involving frequent write operations. By minimizing the number of round-trips to the database, batching improves overall performance, making it suitable for write-heavy applications.

Entity Framework utilizes optimistic concurrency to handle concurrent data access in scalable applications. In this approach, it assumes that conflicts between multiple users are rare, so it allows multiple users to access the data simultaneously. It uses techniques such as timestamp-based checking or version numbers to detect conflicts and resolve them during data updates.

An Entity Type in Entity Framework represents a class that maps to a database table. It defines the structure of the data stored in that table and includes properties that correspond to the table's columns. When working with Entity Framework, developers interact with Entity Types to perform CRUD operations on the underlying database. Understanding Entity Types is fundamental to modeling and querying data effectively in Entity Framework.