AWS Database Specialty (DBS-C01) - Fact Sheet¶

⚠️ RETIRED April 29, 2024. No longer available for new candidates. Replaced by AWS Data Engineer - Associate (DEA-C01) for data-engineering roles. Material preserved as historical reference.

Quick Reference¶

Exam Code: DBS-C01 Duration: 180 minutes Questions: 65 questions Passing Score: 750/1000 Cost: $300 USD Validity: 3 years Difficulty: ⭐⭐⭐⭐⭐

Exam Domains¶

AWS Database Services¶

Relational Databases¶

Amazon RDS - Engines: MySQL, PostgreSQL, MariaDB, Oracle, SQL Server - Multi-AZ: Synchronous replication, automatic failover < 60 sec - Read Replicas: Up to 15, asynchronous, cross-region - Automated backups: 0-35 days retention - Manual snapshots: Unlimited retention - 📖 RDS Documentation - Main documentation hub - 📖 RDS User Guide - Complete user guide - 📖 RDS Multi-AZ - High availability - 📖 Read Replicas - Read scaling - 📖 RDS Backup - Automated backups - 📖 RDS Snapshots - Manual snapshots - 📖 RDS Proxy - Connection pooling

Amazon Aurora - MySQL and PostgreSQL compatible - 5x MySQL, 3x PostgreSQL performance - Storage: Auto-scales 10GB to 128TB - 6 copies across 3 AZs - Up to 15 read replicas - Global Database: < 1 sec cross-region replication, 5 secondary regions - Serverless v2: Scales 0.5-128 ACUs - 📖 Aurora Documentation - Complete Aurora guide - 📖 Aurora Best Practices - Performance and optimization - 📖 Aurora Global Database - Multi-region - 📖 Aurora Serverless - Auto-scaling - 📖 Aurora Replicas - Read scaling - 📖 Aurora Backup - Backup and restore

NoSQL Databases¶

Amazon DynamoDB - Key-value and document database - Single-digit millisecond latency - Capacity modes: On-demand or provisioned - Global Tables: Multi-region active-active - DynamoDB Streams: Change data capture - DAX: In-memory cache, microsecond latency - Backup: On-demand and PITR (point-in-time recovery) - 📖 DynamoDB Documentation - Main documentation hub - 📖 DynamoDB Developer Guide - Complete developer guide - 📖 Global Tables - Multi-region replication - 📖 DynamoDB Streams - Change data capture - 📖 DAX - In-memory acceleration - 📖 DynamoDB Backup - Backup and PITR - 📖 Capacity Modes - On-demand vs provisioned

Amazon DocumentDB - MongoDB 3.6/4.0/5.0 compatible - Fully managed, scales to millions of requests/sec - Storage: Auto-scales to 128TB - 6-way replication across 3 AZs - 📖 DocumentDB Documentation - Complete guide - 📖 DocumentDB Developer Guide - Getting started - 📖 DocumentDB Clusters - Cluster management - 📖 DocumentDB Backup - Backup strategies

Amazon Keyspaces - Apache Cassandra compatible - Serverless, pay-per-request - Single-digit millisecond latency at scale - PITR up to 35 days - 📖 Keyspaces Documentation - Main documentation - 📖 Keyspaces Developer Guide - Complete guide - 📖 Keyspaces Data Modeling - Table design - 📖 Keyspaces PITR - Point-in-time recovery

Specialized Databases¶

Amazon Neptune - Graph database - Property graph (Gremlin) and RDF (SPARQL) - Use cases: Knowledge graphs, fraud detection, social networks - 📖 Neptune Documentation - Main documentation hub - 📖 Neptune User Guide - Complete user guide - 📖 Neptune Graph Data Model - Property graphs and RDF - 📖 Gremlin Query Language - Graph traversal queries - 📖 SPARQL Query Language - RDF queries - 📖 Neptune High Availability - Cluster architecture

Amazon Timestream - Time-series database - 1,000x faster, 1/10^th cost vs relational DBs for time-series - Automatic tiering: Memory to magnetic storage - 📖 Timestream Documentation - Main documentation hub - 📖 Timestream Developer Guide - Complete developer guide - 📖 Timestream Data Model - Time-series design - 📖 Timestream Queries - Query examples - 📖 Timestream Scheduled Queries - Automated queries

Amazon QLDB - Quantum Ledger Database - Immutable, cryptographically verifiable transaction log - Use cases: Compliance, audit trails, supply chain - 📖 QLDB Documentation - Main documentation hub - 📖 QLDB Developer Guide - Complete developer guide - 📖 QLDB Journal - Immutable transaction log - 📖 QLDB PartiQL - SQL-compatible query language - 📖 QLDB Verification - Cryptographic verification

Amazon MemoryDB for Redis - Redis-compatible in-memory database - Microsecond read, single-digit millisecond write latency - Durable with Multi-AZ - 📖 MemoryDB Documentation - Main documentation hub - 📖 MemoryDB Developer Guide - Complete developer guide - 📖 MemoryDB Durability - Multi-AZ durability - 📖 MemoryDB Snapshots - Backup and restore - 📖 MemoryDB Redis Compatibility - Redis commands

Data Warehousing¶

Amazon Redshift - Petabyte-scale data warehouse - Columnar storage, MPP architecture - RA3 nodes: Managed storage, scales compute/storage independently - Redshift Spectrum: Query S3 directly - Concurrency Scaling: Handle burst traffic - 📖 Redshift Documentation - Main documentation hub - 📖 Redshift Management Guide - Cluster management - 📖 Redshift Database Developer Guide - SQL and optimization - 📖 Redshift Clusters - Cluster management - 📖 Redshift Distribution Styles - KEY, EVEN, ALL - 📖 Redshift Sort Keys - Query optimization - 📖 Redshift Spectrum - Query S3 data - 📖 Redshift Concurrency Scaling - Handle query spikes - 📖 Redshift WLM - Workload management - 📖 Redshift Snapshots - Backup and restore

Caching¶

Amazon ElastiCache - Redis: Advanced data structures, persistence, backup, Multi-AZ - Memcached: Simple key-value, multi-threaded, no persistence - 📖 ElastiCache Documentation - Main documentation hub - 📖 ElastiCache User Guide - Complete user guide - 📖 ElastiCache for Redis - Redis features - 📖 ElastiCache for Memcached - Memcached features - 📖 Redis Replication - Multi-AZ with failover - 📖 Redis Cluster Mode - Sharding for scale - 📖 ElastiCache Backup - Snapshots and restore

Database Migration¶

AWS Database Migration Service (DMS) - Homogeneous: Oracle → Oracle, MySQL → Aurora MySQL - Heterogeneous: Oracle → PostgreSQL (requires SCT) - Continuous replication (CDC - Change Data Capture) - Zero downtime migration - 📖 DMS Documentation - Main documentation hub - 📖 DMS User Guide - Complete migration guide - 📖 DMS Migration Planning - Best practices - 📖 DMS Endpoints - Source and target configuration - 📖 DMS Replication Tasks - Migration tasks - 📖 DMS CDC - Change data capture - 📖 DMS Monitoring - Task monitoring

Schema Conversion Tool (SCT) - Converts database schemas - Oracle/SQL Server → Aurora/RDS PostgreSQL/MySQL - Assessment report for migration complexity - 📖 SCT Documentation - Main documentation hub - 📖 SCT User Guide - Complete user guide - 📖 SCT Installation - Setup guide - 📖 SCT Assessment Report - Migration complexity - 📖 SCT Conversion Rules - Schema conversion

Database Design Patterns¶

Choosing the Right Database¶

Relational (RDS/Aurora): - ✅ ACID transactions, complex queries, joins, foreign keys - Use cases: ERP, CRM, e-commerce transactions

Key-Value (DynamoDB): - ✅ Single-digit ms latency, massive scale, simple access patterns - Use cases: Session store, user profiles, real-time bidding

Document (DocumentDB): - ✅ Flexible schema, nested documents, MongoDB compatibility - Use cases: Content management, catalogs, user profiles

Graph (Neptune): - ✅ Relationship queries, connected data - Use cases: Social networks, recommendation engines, fraud detection

Time-Series (Timestream): - ✅ Time-stamped data, high ingestion rate - Use cases: IoT, DevOps metrics, financial trading

Ledger (QLDB): - ✅ Immutable, cryptographically verifiable - Use cases: Financial transactions, supply chain, audit logs

Performance Optimization¶

RDS/Aurora¶

• Read replicas for read scaling
• Provision IOps (io1/io2) for consistent performance
• Parameter groups for tuning
• Enhanced Monitoring for OS metrics
• Performance Insights for query analysis
• Connection pooling with RDS Proxy
• 📖 RDS Performance Insights - Query performance analysis
• 📖 RDS Enhanced Monitoring - OS-level metrics
• 📖 RDS Parameter Groups - Database tuning
• 📖 Aurora Performance - Optimization techniques
• 📖 RDS Storage Types - IOPS provisioning

DynamoDB¶

• Partition key design: High cardinality, uniform access
• GSI for alternate access patterns
• LSI for sort key variations (must create at table creation)
• DynamoDB Accelerator (DAX) for caching
• Auto Scaling for capacity management
• On-demand for unpredictable workloads
• 📖 DynamoDB Partition Keys - Key design best practices
• 📖 Global Secondary Indexes - GSI design and usage
• 📖 Local Secondary Indexes - LSI design and usage
• 📖 DynamoDB Auto Scaling - Automatic capacity management
• 📖 DynamoDB Performance - Performance optimization

Redshift¶

• Distribution styles: KEY, EVEN, ALL
• Sort keys for range queries
• Compression encoding
• Vacuum and analyze regularly
• Workload Management (WLM) for query prioritization
• Result caching
• 📖 Redshift Table Design - Distribution and sort keys
• 📖 Redshift Vacuum - Reclaim storage
• 📖 Redshift Analyze - Update statistics
• 📖 Redshift Compression - Column encoding
• 📖 Redshift Query Performance - Optimization techniques

Security Best Practices¶

Encryption - At rest: KMS encryption (enable at creation) - In transit: SSL/TLS for all connections - Transparent Data Encryption (TDE) for Oracle/SQL Server - 📖 RDS Encryption - Encryption at rest - 📖 DynamoDB Encryption - Encryption at rest - 📖 KMS Key Management - Customer managed keys

Network Security - VPC for network isolation - Security groups for access control - Private subnets for database instances - VPC endpoints for S3/DynamoDB access - 📖 RDS VPC - VPC configuration - 📖 VPC Security Groups - Access control - 📖 VPC Endpoints - Private connectivity

Access Control - IAM database authentication (RDS/Aurora) - IAM policies for service-level control - Database users for application access - Least privilege principle - Secrets Manager for credential rotation - 📖 IAM Database Authentication - Token-based auth - 📖 DynamoDB IAM - Fine-grained access - 📖 Secrets Manager - Credential rotation - 📖 RDS Master User - Master account management

Auditing - CloudTrail for API calls - Database audit logs - Enhanced Monitoring - Performance Insights - CloudWatch Logs - 📖 RDS Database Logs - Log file access - 📖 DynamoDB CloudTrail - API logging - 📖 Aurora Audit Logs - Database activity - 📖 CloudWatch Logs Insights - Log analysis

Common Scenarios¶

Scenario 1: High Write Throughput - Solution: DynamoDB with on-demand capacity or Aurora Serverless v2

Scenario 2: Complex Analytics - Solution: Redshift or Athena (query S3)

Scenario 3: Multi-Region DR - Solution: Aurora Global Database or DynamoDB Global Tables

Scenario 4: Heterogeneous Migration - Solution: DMS + SCT with continuous replication

Scenario 5: Caching Layer - Solution: ElastiCache Redis (complex data) or DAX (DynamoDB-specific)

Essential Documentation¶

• 📖 AWS Database Blog
• 📖 Database Migration Guides
• 📖 RDS Best Practices
• 📖 DynamoDB Best Practices
• 📖 Aurora Best Practices

Exam Tips¶

Keywords: - "High availability" → Multi-AZ, Aurora, Global Tables - "Read-heavy" → Read replicas, DAX, ElastiCache - "Write-heavy" → DynamoDB, Aurora Serverless - "Analytics" → Redshift, Athena, Redshift Spectrum - "Migration" → DMS, SCT, Snowball (large data) - "Graph data" → Neptune - "Time-series" → Timestream

Focus Areas: - Database selection for specific workloads - Performance tuning and optimization - Migration strategies (DMS, SCT) - Backup, recovery, and DR - Security and encryption - Monitoring with Performance Insights

Pro Tip: This exam requires deep database expertise. Know when to use each database type, how to optimize performance, and how to migrate databases. Focus on real-world scenarios and trade-offs!