Cloud Storage and Databases

This topic covers cloud data services. You match object, block and file storage to workloads, compare relational and NoSQL databases and their scaling models, and apply the CAP theorem to reason about consistency versus availability under network partitions.

Relation 1 —

Write this relation with symbols exactly as in Rajaraman Cloud — Standard reference before substituting numbers. Examiners award partial marks for a correct setup even when arithmetic slips.

Relation 2 —

Write this relation with symbols exactly as in Rajaraman Cloud — Standard reference before substituting numbers. Examiners award partial marks for a correct setup even when arithmetic slips.

Relation 3 —

Write this relation with symbols exactly as in Rajaraman Cloud — Standard reference before substituting numbers. Examiners award partial marks for a correct setup even when arithmetic slips.

Different data needs different storage: object storage (bucket and key) is ideal for large, immutable blobs like media and backups with virtually unlimited scale, block storage behaves like a disk for databases and boot volumes needing low latency, and file storage offers shared hierarchical access. Databases split similarly — relational systems favour strong consistency and rich queries but scale mainly by bigger machines, while NoSQL stores shard across nodes for horizontal scale, often relaxing consistency. The CAP theorem sharpens this: when a network partition occurs, a system must choose between consistency and availability, so distributed databases pick their trade-off deliberately. Managed services add durability through cross-zone replication and automate backups and failover.

State assumptions explicitly before using any relation for cloud storage and databases — steady state, uniform properties, linear elastic material, ideal gas, incompressible flow, etc., as applicable.

Wrong assumptions invalidate the entire solution even when the formula is correct. In Cloud Computing viva and GATE descriptive questions, listing valid assumptions often earns separate marks.

1. Read the question and list given data with SI units (common in Cloud Computing papers).
2. Draw a neat labelled diagram where applicable — examiners in Indian universities award diagram marks even when arithmetic slips.
3. Identify which relation from this topic applies to cloud storage and databases.
4. Use equation 1: .
5. Use equation 2: .
6. Substitute values, compute, and verify units and sign (direction).
7. State conclusion in one line — e.g. safe/unsafe, stable/unstable, feasible/infeasible.

Cloud Storage and Databases appears in SaaS/PaaS deployments. In Indian it software curricula this topic is tested because it connects theory to virtualization and cloud service models.

GATE and semester exams often combine cloud storage and databases with earlier units — revise prerequisites before attempting mixed problems.

Industry interview panels sometimes ask: "Where did you use cloud storage and databases?" — answer with a lab, mini-project, or plant visit example if possible.

Students misstate CAP (you cannot sacrifice partition tolerance in a distributed system, so the real choice is C versus A during a partition), pick object storage for low-latency random writes, and assume NoSQL is always faster. Confusing replication (durability/availability) with sharding (scale) is common.

Before attempting cloud storage and databases problems, confirm you can:

1. Durability via replication across availability zones
2. Managed databases handle backups and failover
3. CAP theorem: consistency, availability, partition tolerance trade-off

Revise the solved examples in Rajaraman Cloud — Standard reference and one previous-year GATE or university paper for this unit.