Strategy variants in distributed databases Which strategies can a distributed database use to organize and place data across sites?

Introduction / Context:
Distributed databases offer flexible deployment patterns. Even a “distributed” system may centralize certain critical datasets while providing remote access, replicate hot objects for availability and read scaling, and partition large tables for locality and parallelism. Understanding these options helps architects balance performance, cost, and consistency.

Given Data / Assumptions:

• Diverse workload requirements exist (OLTP, analytics, regional traffic).
• Network latency and bandwidth vary by site.
• The DDBMS or data platform supports multiple placement strategies.

Concept / Approach:

Valid strategies include: centralized storage with distributed access (simplifies management but may add latency), partial/total replication (improves availability and proximity but increases write cost), and partitioning (splits data by rows/columns to scale and localize access). A practical design often combines all three, depending on data domains and SLAs.

Step-by-Step Solution:

Verification / Alternative check:

Architectural guides describe hybrid deployments combining central stores, replicas, and shards to meet diverse requirements.

Why Other Options Are Wrong:

• “None of the above” is false; many systems use each approach.
• “Only full replication” is overly restrictive and inefficient for many workloads.

Common Pitfalls:

• Over-replicating write-heavy datasets, causing latency and conflicts.
• Ignoring data governance and compliance when placing shards globally.

Final Answer:

All of the above.