Cloud engineers juggling multiple AWS database services need quick answers without digging through lengthy documentation. This AWS database cheat sheet gives you instant access to the most important commands, configurations, and best practices for managing databases in the cloud.
Who this guide helps: DevOps engineers, cloud architects, and database administrators who work with AWS daily and need a reliable AWS database reference to speed up their workflow.
You’ll find ready-to-use code snippets and configurations for AWS RDS setup and management, plus a complete Amazon DynamoDB tutorial covering essential operations like table creation, queries, and scaling. We’ll also dive into AWS Aurora features that can boost your database performance, along with proven database migration AWS strategies to move your workloads safely to the cloud.
Keep this AWS database reference bookmarked – it’s designed to get you the information you need in seconds, not minutes.
AWS Relational Database Service (RDS) Essentials
Quick Setup Commands for Popular Database Engines
Launch a MySQL RDS instance using AWS CLI: aws rds create-db-instance --db-instance-identifier mydb --db-instance-class db.t3.micro --engine mysql --master-username admin --master-user-password mypassword123 --allocated-storage 20. For PostgreSQL, replace --engine mysql with --engine postgres. Oracle databases require --engine oracle-ee with higher storage allocation. SQL Server uses --engine sqlserver-ex for Express edition.
Performance Tuning Parameters That Matter
Focus on these critical RDS parameters for optimal performance: innodb_buffer_pool_size should consume 70-80% of available memory for MySQL workloads. Set shared_buffers to 25% of RAM for PostgreSQL instances. Configure max_connections based on your application’s concurrent user requirements. Enable slow_query_log to identify bottlenecks. Adjust read_replica_lag_threshold for read scaling scenarios and monitor db_instance_cpu_utilization closely.
Backup and Recovery Best Practices
Configure automated backups with 7-35 day retention periods using --backup-retention-period parameter. Create manual snapshots before major changes with aws rds create-db-snapshot. Enable point-in-time recovery for granular restoration options. Store cross-region snapshots for disaster recovery compliance. Test restoration procedures monthly to validate backup integrity. Use encrypted snapshots for sensitive data protection and implement backup monitoring alerts.
Multi-AZ Deployment Configuration
Enable Multi-AZ deployment during creation with --multi-az flag or modify existing instances using aws rds modify-db-instance --db-instance-identifier mydb --multi-az. This AWS RDS guide feature provides automatic failover within 60-120 seconds during outages. Primary and standby instances sync synchronously across availability zones. Failover triggers include host failure, network connectivity issues, or storage problems. Read replicas can complement Multi-AZ for read scaling.
Amazon DynamoDB Operations Guide
Table Creation and Schema Design Fundamentals
Design your DynamoDB tables with careful partition key selection to ensure even data distribution across multiple partitions. Choose high-cardinality attributes that spread requests uniformly, avoiding hot partitions that throttle performance. Combine partition keys with sort keys for hierarchical data access patterns. Plan your access patterns upfront since DynamoDB’s NoSQL structure requires different thinking than relational databases – you’ll typically denormalize data and create table structures optimized for your specific query needs rather than normalized schemas.
Query and Scan Optimization Techniques
Query operations target specific partition keys and optionally filter by sort key ranges, making them highly efficient for targeted data retrieval. Always use Query instead of Scan when possible, as Scan operations examine every item in your table and consume significant read capacity. Implement pagination using LastEvaluatedKey to handle large result sets without timeouts. Use projection expressions to retrieve only needed attributes, reducing bandwidth costs. Add filter expressions after Query operations to refine results without additional read capacity consumption on filtered items.
Global Secondary Index Management
Global Secondary Indexes (GSI) enable alternative access patterns beyond your table’s primary key structure. Each GSI maintains its own partition and sort key combination, allowing queries on different attributes. Plan GSI capacity separately from your main table since they consume independent read/write capacity units. Design sparse indexes by only including items with specific attribute values to optimize storage costs. Remember that GSI updates are eventually consistent, so factor this into your application design when immediate consistency is required.
Auto Scaling Configuration for Cost Efficiency
Configure DynamoDB Auto Scaling to automatically adjust read and write capacity based on traffic patterns, preventing over-provisioning during low-traffic periods. Set target utilization percentages between 70-80% to balance cost efficiency with performance headroom for traffic spikes. Define minimum and maximum capacity limits that align with your budget constraints and performance requirements. Monitor CloudWatch metrics like ConsumedReadCapacityUnits and ConsumedWriteCapacityUnits to validate scaling effectiveness. Consider switching to On-Demand billing for unpredictable workloads where traffic patterns vary significantly throughout the day.
DynamoDB Streams Integration Points
DynamoDB Streams capture real-time data modifications, providing ordered records of item-level changes within 24 hours. Configure stream view types to capture keys-only, new images, old images, or both new and old images based on your downstream processing needs. Integrate streams with AWS Lambda for serverless event processing, enabling real-time analytics, data replication, or cross-region synchronization. Use multiple Lambda functions to process different stream record types in parallel. Implement proper error handling and dead letter queues since stream records have limited retention periods and failed processing can result in permanent data loss.
Amazon Aurora Advanced Features
Serverless Configuration for Variable Workloads
Aurora Serverless automatically scales compute capacity based on application demand, making it perfect for unpredictable workloads. Set up involves creating a serverless cluster through the AWS console or CLI, configuring scaling parameters like minimum and maximum Aurora Capacity Units (ACUs), and enabling the Data API for HTTP-based connections. The service pauses during idle periods, resuming within seconds when requests arrive, providing cost optimization for development environments and applications with sporadic traffic patterns.
Cross-Region Replication Setup
Aurora Global Database enables cross-region replication with typical lag times under one second. Create a global cluster by promoting an existing Aurora cluster to global status, then add secondary regions through the RDS console. Each secondary region can have up to 16 read replicas, and failover to a secondary region takes less than one minute. Configure automated backups and point-in-time recovery independently for each region to maintain data durability across geographic locations.
Performance Insights Monitoring Tools
Performance Insights provides database performance monitoring with detailed wait event analysis and top SQL identification. Enable the feature during cluster creation or modify existing Aurora clusters to activate monitoring. The dashboard displays database load charts, top SQL statements by execution count, and wait events categorized by resource bottlenecks. Use the API to extract performance metrics programmatically, set up CloudWatch alarms for performance thresholds, and correlate database performance with application metrics for comprehensive AWS Aurora features monitoring.
Database Migration Strategies
AWS Database Migration Service Setup
AWS Database Migration Service (DMS) simplifies database migration AWS processes by providing a managed service that replicates data between source and target databases. Create a replication instance that acts as the compute resource for your migration, then configure source and target endpoints with connection details. DMS supports homogeneous migrations (Oracle to Oracle) and heterogeneous migrations (Oracle to PostgreSQL) with minimal downtime.
| Migration Type | Use Case | Duration |
|---|---|---|
| One-time | Initial data load | Hours to days |
| Ongoing replication | Real-time sync | Continuous |
| Full load + CDC | Complete migration | Variable |
Schema Conversion Tool Usage
AWS Schema Conversion Tool (SCT) automates database schema conversion between different database engines, making it essential for heterogeneous migrations. Download and install SCT locally, then connect to your source database to analyze schema compatibility. The tool generates conversion reports highlighting incompatible objects and suggests manual fixes. SCT handles stored procedures, functions, triggers, and data types, creating equivalent structures in your target database engine.
Zero-Downtime Migration Techniques
Zero-downtime migrations require careful planning using AWS DMS with Change Data Capture (CDC) to keep source and target databases synchronized. Start with a full load migration during low-traffic periods, then enable CDC to capture ongoing changes. Implement application-level failover using DNS switching or load balancers to redirect traffic to the new database. Test cutover procedures thoroughly and maintain rollback plans to ensure business continuity during migration.
Data Validation and Testing Methods
Data validation ensures migration accuracy through automated and manual verification processes. Use DMS data validation features to compare row counts, column values, and data integrity between source and target databases. Implement custom validation scripts to verify business logic, constraints, and application-specific data transformations. Run comprehensive testing including unit tests, integration tests, and performance benchmarks before declaring migration successful. Monitor replication lag and validate that all critical business processes function correctly with the migrated data.
Security and Access Control Best Practices
IAM Roles and Policies for Database Access
Creating least-privilege IAM policies ensures your AWS database security stays tight. Grant database-specific permissions using resource-based policies that target specific RDS instances or DynamoDB tables. Service-linked roles automatically handle permissions for AWS services, while custom policies let you fine-tune access controls. Always use temporary credentials through IAM roles rather than embedding access keys in applications. Cross-account access requires trust relationships and proper resource ARN specifications for secure database connectivity.
Encryption at Rest and in Transit Configuration
Enable encryption at rest during database creation using AWS KMS keys – you can’t add it later for most services. RDS supports both AWS-managed and customer-managed encryption keys, while DynamoDB offers server-side encryption by default. Configure SSL/TLS certificates for in-transit encryption across all database connections. Aurora automatically encrypts backups, snapshots, and read replicas when the primary database uses encryption. Monitor certificate expiration dates and rotate encryption keys regularly to maintain security compliance.
VPC Security Group Rules for Database Isolation
Database security groups should only allow traffic from specific application tiers on required ports. Create separate security groups for web, application, and database layers with explicit rules between each tier. Never open database ports (3306, 5432, 1433) to 0.0.0.0/0 – use private subnets instead. Reference security groups by ID rather than IP ranges to maintain dynamic connectivity as instances scale. Configure NACLs as an additional layer of subnet-level protection alongside security group rules for comprehensive network isolation.
Monitoring and Troubleshooting Tools
CloudWatch Metrics for Database Performance
CloudWatch provides essential metrics for AWS database monitoring including CPU utilization, read/write IOPS, database connections, and buffer cache hit ratios. Set up custom dashboards to track RDS performance metrics like DatabaseConnections, CPUUtilization, and ReadLatency for proactive monitoring. Configure alarms for critical thresholds to get instant notifications when performance degrades.
| Metric | Purpose | Threshold |
|---|---|---|
| CPUUtilization | Track processor load | >80% warning |
| DatabaseConnections | Monitor connection pool | >75% of max |
| ReadLatency/WriteLatency | Identify I/O bottlenecks | >20ms concern |
Database Activity Streams Configuration
Database Activity Streams capture real-time database activity for auditing and compliance purposes in RDS for Oracle and SQL Server. Enable streams through the RDS console or CLI to monitor user activities, failed login attempts, and data access patterns. Configure the activity stream to send logs to Kinesis Data Streams for real-time analysis or store in S3 for long-term retention and compliance reporting.
Common Performance Issues and Quick Fixes
Slow query performance often stems from missing indexes, inefficient queries, or resource constraints. Use Performance Insights to identify top SQL statements consuming resources and optimize accordingly. For DynamoDB, watch for hot partitions and implement proper partition key design. Scale RDS instances vertically or enable read replicas for read-heavy workloads.
Quick Fixes:
- Enable automated backups and Multi-AZ for high availability
- Optimize connection pooling to reduce overhead
- Review slow query logs and add appropriate indexes
- Configure parameter groups for workload-specific tuning
Cost Optimization Monitoring Techniques
Track database costs using AWS Cost Explorer and set up billing alerts for unexpected spend increases. Monitor unused RDS instances, oversized storage, and idle connections that drive unnecessary costs. Use Reserved Instances for predictable workloads and consider Aurora Serverless for variable traffic patterns. Enable storage autoscaling to avoid over-provisioning while maintaining performance during peak loads.
Cost Monitoring Checklist:
- Review monthly RDS and DynamoDB usage reports
- Identify underutilized database instances for rightsizing
- Monitor data transfer costs between availability zones
- Set up budget alerts for each database service
AWS databases can feel overwhelming with all the different options and configurations available, but having the right reference points makes all the difference. From RDS basics to DynamoDB operations, Aurora’s high-performance features to seamless migration strategies, these tools become much more manageable when you know the key commands and best practices. Security and monitoring shouldn’t be afterthoughts either – they’re what keep your databases running smoothly and your data protected.
Keep this cheatsheet handy during your next AWS project and bookmark the specific sections you use most often. The more you work with these database services, the more natural these operations will become. Start with the basics, master one service at a time, and gradually expand your toolkit as your projects grow in complexity.