DBMS Topics
HubUnit 1 — Introduction to DBMS & Entity-Relationship ModelAdvantages and Disadvantages of DBMSAggregationCharacteristics of DBMSData IndependenceData ModelsDatabase Users and AdministratorsDBMS LanguagesER DiagramsEntity-Relationship ER ModelIntroduction to DBMSKeys in DBMSRelationships and CardinalitySchema and InstanceSpecialization and GeneralizationUnit 1 Summary — Introduction to DBMS & ER ModelThree-Level Architecture ANSI/SPARC ArchitectureWeak Entity SetUnit 2 — Relational Model & SQLConstraints in SQLCursorsDCL and TCL CommandsDDL CommandsDML CommandsDomain Relational Calculus DRCFunctions and Stored ProceduresJoins in SQLNested Queries SubqueriesRelational AlgebraRelational CalculusRelational ModelSQL IntroductionUnit 2 Summary — Relational Model & SQLTriggers in SQLTuple Relational Calculus TRCViews in SQLArmstrong's Axioms & Closure of AttributesBoyce-Codd Normal Form BCNFClosure of AttributesDependency PreservationFifth Normal Form 5NFFirst Normal Form 1NFFourth Normal Form 4NFFunctional DependencyLossless DecompositionMultivalued DependencyNormal Forms — OverviewNormalization — IntroductionSecond Normal Form 2NFUnit 3 Summary — NormalizationThird Normal Form 3NFUnit 4 — Transaction Management & Concurrency ControlACID PropertiesCheckpointsConcurrency Control & SerializabilityDeadlock in DBMSLock-Based Protocols & Two-Phase LockingLog-Based RecoveryRecovery SystemSerializabilityShadow PagingUnit 4 Summary — Transaction Management & Concurrency ControlTimestamp-Based ProtocolTransaction ManagementTransaction StatesTwo-Phase Locking 2PLUnit 5 — Storage & IndexingB+ TreeB-TreeCost EstimationDynamic HashingFile OrganizationHashing TechniquesIndexing — IntroductionMulti-Level IndexingQuery OptimizationQuery ProcessingSingle-Level IndexingStatic HashingUnit 5 Summary — Storage & IndexingUnit 6 — Advanced Topics in DBMSBig Data and DBMSCloud DatabaseData MiningData WarehouseDatabase SecurityDistributed DatabaseDistributed Query ProcessingDistributed DBMS Architecture, Fragmentation & ReplicationMongoDB — IntroductionNoSQL DatabaseReplication in Distributed DatabasesUnit 6 Summary — Advanced Topics in DBMSViva Voce — DBMSImportant Viva Questions — DBMSInterview Questions — DBMSViva Answers — DBMSDBMS Multiple Choice Questions MCQsDBMS Lab Programs
Cloud Database
Last Updated : 21 May, 2026
A cloud database is a database service built, deployed, and accessed through a cloud computing platform. It provides database capabilities as a service DBaaS — Database a
Unit 6125 words12 headingsExamples included
What is a Cloud Database?
A cloud database is a database service built, deployed, and accessed through a cloud computing platform. It provides database capabilities as a service (DBaaS — Database as a Service) over the internet, managed by a cloud provider.
Users pay for what they use, scale on demand, and the provider handles infrastructure, patching, backups, and availability.
Cloud Database Models
1. Database as a Service (DBaaS)
The cloud provider manages the entire database infrastructure:
| User handles | Application code + data |
| Provider handles | Hardware, OS, DB software, patches, |
| Amazon RDS | MySQL, PostgreSQL, Oracle, SQL Server on AWS |
| Azure SQL DB | SQL Server on Microsoft Azure |
| Google Cloud SQL | MySQL, PostgreSQL on GCP |
| Amazon DynamoDB | Managed NoSQL (key-value + document) |
| MongoDB Atlas | Managed MongoDB |
2. Self-Managed on Cloud VM
User installs and manages the database on a cloud virtual machine:
| User handles | DB software, config, backups, patches, scaling |
| Provider handles | Hardware, network, virtualization |
| Example | Launch an EC2 instance on AWS, install MySQL yourself |
Major Cloud Database Services
| │ RDS | Managed relational (MySQL, Postgres) │ |
| │ DynamoDB | Managed NoSQL (key-value + document) │ |
| │ Redshift | Managed data warehouse │ |
| │ Aurora | MySQL/PostgreSQL-compatible, cloud- │ |
| │ ElastiCache | Managed Redis/Memcached (caching) │ |
| │ Neptune | Managed graph database │ |
| │ Azure SQL Database | Managed SQL Server │ |
| │ Azure Cosmos DB | Multi-model NoSQL (global) │ |
| │ Azure Synapse | Analytics + data warehouse │ |
| │ Cloud SQL | Managed MySQL/PostgreSQL │ |
| │ BigQuery | Serverless data warehouse │ |
| │ Firestore | NoSQL document database │ |
| │ Cloud Spanner | Globally distributed RDBMS │ |
Advantages of Cloud Databases
| 1. No hardware purchase | Pay-as-you-go; CAPEX → OPEX |
| 2. Automatic scaling | Scale up/down in minutes |
| 3. High availability | Multi-AZ deployments; 99.99% SLA |
| 4. Managed backups | Automated daily backups; PITR |
| 5. Auto-patching | No manual DB software updates |
| 6. Global distribution | Deploy in 25+ regions worldwide |
| 7. Fast deployment | New DB instance ready in minutes |
| 8. Built-in monitoring | Metrics, alerts, performance insights |
| 9. Security built-in | Encryption at rest and in transit |
| 10. Disaster recovery | Cross-region replication available |
Disadvantages of Cloud Databases
| 1. Cost at scale | Can become expensive for large workloads |
| 2. Vendor lock-in | Migrating from AWS Aurora to Azure = complex |
| 3. Network latency | Extra round-trip vs on-premise co-located DB |
| 4. Limited control | Cannot access underlying OS/hardware |
| 5. Data sovereignty | Regulations may restrict data location |
| 6. Internet dependency | Outage if network fails |
Cloud Database Architecture Concepts
Multi-AZ (High Availability)
| AZ-1 (Mumbai) | AZ-2 (Hyderabad) | |||
|---|---|---|---|---|
| PRIMARY DB | ───► | STANDBY DB | ||
| (writes) | sync | (auto-failover) |
Read Replicas (Scaling Reads)
┌─────────────────────────────────────────────┐
│ │
│ Primary DB ──async replication──► Read │
│ (handles writes) Replica 1│
│ ──async──────► Read │
│ Replica 2│
│ ──async──────► Read │
│ Replica 3│
└─────────────────────────────────────────────┘
App servers send writes to Primary, reads to Replicas
→ Distributes read load across multiple replicas
Serverless Databases
| Traditional | Pay for provisioned instances (24/7) |
| Serverless | Pay only when queries are running |
| Amazon Aurora Serverless v2 | auto-scales to 0 when idle |
| Google BigQuery | pay per query, no provisioning |
| PlanetScale | MySQL-compatible, serverless |
| Best for | Dev/test environments, variable workloads, |
Cloud Database Security
Encryption
- At Rest: AES-256 encryption of stored data (default on most services)
- In Transit: TLS/SSL for all connections
Network Isolation
- VPC (Virtual Private Cloud): DB in private subnet, not public internet
- Security Groups: Firewall rules on which IPs/ports can connect
Access Control
- IAM (Identity and Access Management): cloud-level permissions
- DB-level users/roles: GRANT/REVOKE as usual
Audit Logging
- All queries logged for compliance (CloudTrail on AWS)
Exam Focus
Revise definitions, diagrams, examples, and short-answer points for Cloud Database.
Interview Use
Prepare one clear explanation, one practical example, and one common mistake for this DBMS topic.
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