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Distributed Databases:
- Definition: Distributed databases store data across multiple nodes in a network, enabling improved scalability, fault tolerance, and performance compared to centralized databases.
- Fragmentation: Data fragmentation involves dividing the database into smaller subsets or fragments distributed across different nodes. This allows for parallel processing and efficient data retrieval.
- Replication: Replication involves creating and maintaining copies of data across multiple nodes. It enhances fault tolerance and availability by ensuring data redundancy and allowing for failover mechanisms.
- Sharding: Sharding, also known as horizontal partitioning, involves dividing data horizontally based on a sharding key and distributing it across multiple nodes. This strategy improves scalability and performance by spreading the workload evenly across nodes.
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Advantages of Distributed Databases:
- Scalability: Distributed databases can scale horizontally by adding more nodes, allowing them to handle increasing data volumes and user loads.
- Fault Tolerance: With data distributed across multiple nodes, distributed databases can tolerate node failures without compromising data availability or integrity.
- Performance: By distributing data closer to where it's needed, distributed databases can improve query response times and overall system performance.
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Challenges of Distributed Databases:
- Complexity: Designing and managing distributed databases require dealing with additional complexities such as data distribution, replication, and concurrency control.
- Consistency: Ensuring data consistency across distributed nodes is challenging and often requires implementing sophisticated consistency models or distributed consensus algorithms.
- Network Overhead: Communication overhead between distributed nodes can impact performance and scalability, requiring optimization strategies to minimize latency and bandwidth usage.
Paulet Wairagu
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Distributed Databases
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