Definition: A distributed database is a collection of multiple logically interrelated databases distributed over a computer network.

Why Distributed Databases? To overcome limitations of centralized systems such as performance bottlenecks, single point of failure, and scalability issues.

Advantages: Improved performance, higher reliability, better availability, and easier expansion.

Types: Homogeneous vs Heterogeneous Distributed Databases.

Architecture Models: Client-Server, Peer-to-Peer, and Multi-tier architectures.

Components: Local DBMS, Global Schema, Fragmentation Schema, and Allocation Schema.

Design Issues: Data distribution, fragmentation, replication, and allocation.

Design Strategies: Top-down and Bottom-up design approaches.

Types of Fragmentation: Horizontal, Vertical, and Hybrid Fragmentation.

Allocation: Data allocation techniques and factors affecting allocation decisions.

Correctness Rules: Completeness, Reconstruction, and Disjointness.

Replication: Full vs Partial replication.

Consistency Models: Strong consistency, eventual consistency, and CAP Theorem.

Replication Techniques: Synchronous and Asynchronous replication.

Distributed Transactions: ACID properties in distributed environment.

Commit Protocols: Two-Phase Commit (2PC) and Three-Phase Commit (3PC).

Concurrency Control: Distributed locking, timestamp-based protocols.

Query Processing Steps: Parsing, optimization, and execution in distributed systems.

Distributed Query Optimization: Cost-based optimization and semi-join techniques.

NoSQL Types: Key-Value, Document, Column-family, and Graph databases.

CAP Theorem: Consistency, Availability, and Partition Tolerance.

Emerging Trends: NewSQL, Big Data databases, and Cloud databases.

Security Issues: Authorization, authentication, and access control in distributed systems.

Advanced Topics: Database recovery, parallel databases, and current research issues.