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Build these AWS database mini projects in sequence: start with an Amazon RDS connection, then explore Aurora, DynamoDB, and ElastiCache before combining a database with a cache. Each lab focuses on a different data model or operational skill. You’ll need an AWS account and appropriate permissions for hosted projects; charges may apply while resources are running.
Choose a project by what you want to learn
RDS and Aurora teach relational SQL and database connectivity. DynamoDB introduces a managed table-based NoSQL model. ElastiCache adds an in-memory layer for data that an application reads frequently. Start with one service at a time, then try the combined Aurora and ElastiCache demonstration.
| Project | Data model or role | Primary learning objective | Deployment path |
|---|---|---|---|
| RDS first database | Relational SQL | Instance setup, networking, and client connections | Managed DB instance |
| Aurora in a VPC | Relational SQL | Cluster connectivity and application integration | Managed cluster and web server in a VPC |
| Aurora operations | Relational SQL | Endpoints, snapshots, and scaling observations | Aurora cluster |
| DynamoDB tracker or catalog | Managed tables | Table design and application access | Hosted DynamoDB or DynamoDB Local for local development and testing |
| ElastiCache read path | In-memory cache | Understand cached reads versus persistent database reads | Serverless cache or designed cache cluster |
| Aurora plus ElastiCache | Relational database plus in-memory cache | Separate durable records from cacheable reads | Aurora cluster with an ElastiCache cache |
1. Create an RDS database and connect to it
This beginner project teaches the basic learning unit of RDS: a DB instance. Follow the Amazon RDS getting-started guide to create a small MySQL or PostgreSQL database, connect with a database client, and create a simple schema such as a table of books or tasks.
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#1 Best Overall
When you finish practicing, delete the DB instance and any other resources you created for the lab. Leaving hosted resources running can incur charges.
2. Put an Aurora cluster and web server in a VPC
For an Aurora hands-on tutorial, use AWS’s Aurora tutorials to create a cluster and web server in a VPC. The goal is to see how an application reaches a relational database and to make a request that reads and writes application data.
After the basic flow works, extend the lab with one operational task: restore a cluster from a snapshot, or use EventBridge to log a DB instance state change. These additions help connect application development with routine database operations.
3. Explore Aurora endpoints and operations
Use the Aurora cluster and reader endpoints to explore how an application can direct different database work. Practice writes and DDL through the cluster endpoint, then use the reader endpoint for query-intensive sessions. Observe how behavior changes when you adjust replicas or instance classes.
Rank #3
AWS frames this kind of work as an evaluation against an intended use case. A tutorial-scale exercise can teach endpoint and operations concepts, but it does not establish production capacity or predict performance for a real workload.
4. Build a small DynamoDB-backed application
For a DynamoDB getting started project, make a simple tracker or catalog whose records live in a DynamoDB table. AWS’s DynamoDB getting-started guide covers connecting to, creating, and managing tables. The tracker or catalog is a project idea: define a small set of attributes, then build the application around the table operations it needs.
If you want to develop and test without accessing the hosted web service, use DynamoDB Local. For hosted use, check current pricing and any applicable free-tier terms; standard usage charges can apply after free-tier benefits are exhausted. Remove hosted resources when the exercise is complete.
5. Add an ElastiCache layer to a read-heavy flow
An ElastiCache tutorial can teach why applications sometimes keep frequently read data in memory rather than fetching it from a persistent database on every request. AWS describes ElastiCache as an in-memory caching service for accelerating application and database performance. Start with a serverless cache or a designed cache cluster, and follow the relevant AWS learning path for Valkey, Redis OSS, or Memcached.
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Build a small read-heavy application flow and compare two paths: a request served from the cache and one that reads from the persistent database. Treat the cache as a performance layer, not durable storage. The exercise is about observing application behavior, not claiming a particular latency or performance improvement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Combine Aurora and ElastiCache
Once the services make sense separately, build a relational-backed application with a cache layer. AWS documents a path for creating an ElastiCache cache using settings from an Aurora DB cluster. Keep the distinction clear in the application: the database holds persistent records, while selected reads may be served from the cache.
Check engine and Region constraints before deploying. Feature availability can vary by engine version and AWS Region; AWS’s Aurora cross-Region guidance illustrates why current regional support matters. Review current AWS pricing and regional availability, and delete the lab resources when finished.
Plan cleanup before you launch
These are learning exercises, not production-ready architectures or promises of free usage. Before starting a hosted lab, check the current AWS service documentation for supported engines, versions, and Regions, review pricing, and confirm you have the permissions and network access the setup requires. When finished, remove the resources you launched, including instances, clusters, caches, and supporting resources that are no longer needed.
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