Pass the AWS Certified Solutions Architect Associate Certification SAA-C03-(Episode 20: AWS Databases)
📌 Notice
Welcome to the series of blog in my AWS Certified Solutions Architect Associate (SAA-C03) exam preparation series! If you’re looking to pass this challenging yet rewarding certification, you’re in the right place.
Throughout this blog series, you’ll master core AWS architecture concepts — from IAM security fundamentals to advanced VPC networking, cost-optimized EC2 deployments, serverless patterns with Lambda, and multi-region disaster recovery strategies. We’ll break down all key services (S3, RDS, CloudFront etc.) through real-world solution architectures and exam-focused scenarios. Each post will include hands-on walkthroughs, pro tips for the SAA-C03 exam, and best practices used by AWS professionals. Get ready to transform from AWS beginner to certified Solutions Architect!
Note : The blog will be updated with the extra questions and CDK Implementation in a timely manner
🌟 Introduction
In today’s cloud-driven world, selecting the right database is crucial for building scalable, performant, and cost-efficient applications. AWS offers a wide range of managed database services, each designed for specific use cases. Whether you need a relational database for structured data, a NoSQL solution for flexible schemas, or a specialized database for time-series or graph data, AWS has you covered.
This blog explores the key AWS database services, their features, and ideal use cases to help you make an informed decision based on your application’s requirements.
Choosing the Right Database
- Workload Type: Read-heavy, write-heavy, or balanced? What are the throughput needs?
- Scalability: Will traffic fluctuate? Does the database need to scale dynamically?
- Data Size & Growth: How much data will be stored? What’s the average object size?
- Access Patterns: How is data accessed? Random or sequential?
- Durability & Availability: Is this the source of truth? What are the replication needs?
- Latency Requirements: What are the response time expectations? How many concurrent users?
- Data Model: Structured, semi-structured, or unstructured? Need SQL, joins, or flexible schemas?
- Cost & Licensing: Should you use open-source or AWS-native solutions like Aurora?
AWS Database Types & Use Cases
- Relational Databases
- NoSQL Databases
- Object Store
- Data warehouse
- Search
- Graphs
- Ledger
- Time Series
1. Amazon RDS
- Supports PostgreSQL, MySQL, Oracle, SQL Server, MariaDB, and DB2.
Features:
- Automated backups (up to 35 days), Multi-AZ deployments, Read Replicas.
- Security via IAM, KMS, and VPC.
- Manual snapshots for long-term retention.
- Auto-scaling capability for Storage
- Managed and Scheduled maintenance (with downtime)
- RDS Custom for access to and customize the underlying instance (Oracle & SQL Server)
Use Case: Traditional applications requiring SQL, ACID transactions, and relational models.
- Hands — On — Click Here
2. Amazon Aurora
- Supports AWS-optimized MySQL/PostgreSQL-compatible database.
Features:
- Auto-scaling storage (up to 128TB) data is stored in 6 replicas, across 3 AZ — highly available, self-healing
- Aurora Serverless for unpredictable workloads.
- Global Aurora for low-latency reads across regions.
- Compute: Cluster of DB Instance across multiple AZ, auto-scaling of Read Replicas
- Cluster: Custom endpoints for writer and reader DB instances
- Same security / monitoring / maintenance features as RDS
- Aurora Global: up to 16 DB Read Instances in each region, < 1 second storage replication
- Aurora Machine Learning: perform ML using SageMaker & Comprehend on Aurora
- Aurora Database Cloning: new cluster from existing one, faster than restoring a snapshot
Use Case: High-performance, low-maintenance relational database with better scalability than RDS.
3. Amazon DynamoDB
- Serverless, key-value/document store with single-digit millisecond latency.
Features:
- AWS proprietary technology, managed serverless NoSQL database, millisecond latency
- Capacity modes: provisioned capacity with optional auto-scaling or on-demand capacity
- Can replace ElastiCache as a key/value store (storing session data for example, using TTL feature)
- Highly Available, Multi AZ by default, Read and Writes are decoupled, transaction capability
- DAX cluster for read cache, microsecond read latency
- Security, authentication and authorization is done through IAM
- Event Processing: DynamoDB Streams to integrate with AWS Lambda, or Kinesis Data Streams
- Global Table feature: active-active setup
- Automated backups up to 35 days with PITR (restore to new table), or on-demand backups
- Export to S3 without using RCU within the PITR window, import from S3 without using WCU
- Great to rapidly evolve schemas
Use Case: Serverless apps, session storage, distributed caching, high scale applications.
4. Amazon ElastiCache
- Managed Redis / Memcached (similar offering as RDS, but for caches)
- In-memory data store, sub-millisecond latency
- Select an ElastiCache instance type (e.g., cache.m6g.large)
- Support for Clustering (Redis) and Multi AZ, Read Replicas (sharding)
- Security through IAM, Security Groups, KMS, Redis Auth
- Backup / Snapshot / Point in time restore feature
- Managed and Scheduled maintenance
- Requires some application code changes to be leveraged
Use Case: Key/Value store, Frequent reads, less writes, cache results for DB queries, store session data for websites, cannot use SQL.
5. Amazon S3
- S3 is a… key / value store for objects
- Great for bigger objects, not so great for many small objects
- Serverless, scales infinitely, max object size is 5 TB, versioning capability
- Tiers: S3 Standard, S3 Infrequent Access, S3 Intelligent, S3 Glacier + lifecycle policy
- Features: Versioning, Encryption, Replication, MFA-Delete, Access Logs…
- Security: IAM, Bucket Policies, ACL, Access Points, Object Lambda, CORS, Object/Vault Lock
- Encryption: SSE-S3, SSE-KMS, SSE-C, client-side,TLS in transit, default encryption
- Batch operations on objects using S3 Batch, listing files using S3 Inventory
- Performance: Multi-part upload, S3 Transfer Acceleration, S3 Select
- Automation: S3 Event Notifications (SNS, SQS, Lambda, EventBridge)
- Use Cases: static files, key value store for big files, website hosting
6. DocumentDB
- Aurora is an “AWS-implementation” of PostgreSQL / MySQL …
- DocumentDB is the same for MongoDB (which is a NoSQL database)
- MongoDB is used to store, query, and index JSON data
- Similar “deployment concepts” as Aurora
- Fully Managed, highly available with replication across 3 AZ
- DocumentDB storage automatically grows in increments of 10GB
- Automatically scales to workloads with millions of requests per seconds
7. Amazon Neptune
- Fully managed graph database
- A popular graph dataset would be a social network
• Users have friends
• Posts have comments
• Comments have likes from users
• Users share and like posts… - Highly available across 3 AZ, with up to 15 read replicas
- Build and run applications working with highly connected datasets — optimized for these complex and hard queries
- Can store up to billions of relations and query the graph with milliseconds latency
- Highly available with replications across multiple AZs
- Great for knowledge graphs (Wikipedia), fraud detection, recommendation engines, social networking
Amazon Neptune — Streams
- Real-time ordered sequence of every change to your graph data
- Changes are available immediately after writing
- No duplicates, strict order
- Streams data is accessible in an HTTP REST API
- Use cases:
• Send notifications when certain changes are made
• Maintain your graph data synchronized in another data store (e.g., S3, OpenSearch, ElastiCache)
• Replicate data across regions in Neptune
8. Amazon Keyspaces (for Apache Cassandra)
- Apache Cassandra is an open-source NoSQL distributed database
- A managed Apache Cassandra-compatible database service
- Serverless, Scalable, highly available, fully managed by AWS
- Automatically scale tables up/down based on the application’s traffic
- Tables are replicated 3 times across multiple AZ
- Using the Cassandra Query Language (CQL)
- Single-digit millisecond latency at any scale, 1000s of requests per second
- Capacity: On-demand mode or provisioned mode with auto-scaling
- Encryption, backup, Point-In-Time Recovery (PITR) up to 35 days
- Use cases: store IoT devices info, time-series data, …
9. Amazon QLDB
- QLDB stands for ”Quantum Ledger Database”
- A ledger is a book recording financial transactions
- Fully Managed, Serverless, High available, Replication across 3 AZ
- Used to review history of all the changes made to your application data over time
- Immutable system: no entry can be removed or modified, cryptographically verifiable
- 2–3x better performance than common ledger blockchain frameworks, manipulate data using SQL
- Difference with Amazon Managed Blockchain: no decentralization component, in accordance with financial regulation rules
10. Amazon Timestream
- Fully managed, fast, scalable, serverless time series database
- Automatically scales up/down to adjust capacity
- Store and analyze trillions of events per day
- 1000s times faster & 1/10th the cost of relational databases
- Scheduled queries, multi-measure records, SQL compatibility
- Data storage tiering: recent data kept in memory and historical data kept in a cost-optimized storage
- Built-in time series analytics functions (helps you identify patterns in your data in near real-time)
- Encryption in transit and at rest
- Use cases: IoT apps, operational applications, real-time analytics, …
Amazon Timestream — Architecture
📊 AWS Database Services Comparison Table
AWS Cloud Practitioner Questions
- Databases in AWS — Click Here
AWS Solution Architect Associate Questions
- AWS SAA-C03 Sample Questions — Click Here
🧾 Conclusion
When selecting an AWS database service, consider data model, query patterns, scalability, and consistency needs. Here’s a simplified guide
Next Episode : “Pass the AWS Certified Solutions Architect Associate Certification SAA-C03! (Episode 21: Data & Analytics )”
