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Distributed Data Design

Quick Summary & Key Takeaways

• Subject Focus: (RDBMS) Relational Database Management System notes in English (BCA / B.Tech Syllabus)
• Core Topic: Distributed Data Design - General Topics
• Overview: What is Distributed Data Design in RDBMS: Distributed Data Design involves spreading a database across multiple physical locations. The database is sto...
• Key Concepts: Distributed data design, Distributed database design Hindi, Data partitioning,

What is Distributed Data Design in RDBMS:

Distributed Data Design involves spreading a database across multiple physical locations. The database is stored on multiple computers, which can be in the same physical location or spread across a network of interconnected computers. The goal is to improve performance, increase availability, and ensure reliability.

Key Concepts:

Data Fragmentation: Breaking the database into smaller pieces.
- Horizontal Fragmentation: Splitting a table by its rows (e.g., customers from one region on one server, customers from another region on a different server).
- Vertical Fragmentation: Splitting a table by its columns (e.g., product names and prices on one server, inventory details on another).
Data Replication: Storing copies of the data on multiple sites. This increases availability and read performance, but adds complexity to write operations.
Data Allocation: Deciding where to store the fragments and replicas.

Frequently Asked Questions (FAQs)

What is Distributed Data Design in RDBMS: Distributed Data Design involves spreading a database across multiple physical locations.

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