Explore the mechanisms for handling distributed deadlock detection and resolution in distributed database environments.

Discuss the CAP theorem and its implications for distributed database systems, highlighting trade-offs between consistency, availability, and partition tolerance.

How do data partitioning techniques impact the fault tolerance of distributed database environments?

Describe the concepts of transparency in distributed database systems, including location transparency, replication transparency, and fragmentation transparency.

Examine the challenges and solutions associated with fault tolerance and recovery in distributed database systems. How can data be recovered after a failure in a distributed environment?

How do auto-scaling mechanisms affect resource management in distributed database environments?

How does the Two-Phase Commit (2PC) protocol work in a distributed database system, and what are its limitations?

The problems with concurrency control in distributed database management systems are similar to those in other types of distributed systems. Do we need to use two-phase locking (2PL) or some other kind of concurrency management when we have a unique setup? Explain.

Explore the concept of data shuffling in distributed computing frameworks like Apache Hadoop and its impact on distributed database performance.

What is the significance of data fragmentation in a distributed database environment?