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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Build these six AWS database mini projects in sequence: start with an Amazon RDS instance, then explore Aurora, DynamoDB and ElastiCache before combining Aurora with a cache. Each lab isolates a different skill—database connections, networking, table design, operational changes or caching—without treating a tutorial deployment as production-ready. You’ll need an AWS account and suitable permissions for hosted labs; check current Regional and engine-version support, review pricing, and remove resources when finished.
Choose a project based on what you want to learn
RDS and Aurora teach relational SQL and the connections and operations around a managed database. DynamoDB introduces a managed table-based data model. ElastiCache is an in-memory performance layer, not a durable database. Begin with one service at a time; combine services only after you understand each one’s role.
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- Connection and networking: Start with the RDS first-instance lab, then try Aurora in a VPC.
- Relational operations: Use Aurora to practice snapshots, endpoints, and instance or replica changes.
- Table-backed application: Build a small tracker or catalog with DynamoDB.
- Caching behavior: Add ElastiCache to a read-heavy flow, then integrate it with Aurora.
Project 1: Create and connect to an Amazon RDS database
Follow the Amazon RDS getting-started guide to create a small MySQL or PostgreSQL DB instance, connect with a database client, and create a simple schema. The guide lists Db2, MariaDB, MySQL, Microsoft SQL Server, Oracle, and PostgreSQL engine paths; consult the live guide for current options.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The learning goal is to understand that a DB instance is more than an engine choice. During setup, choose the engine, storage and instance class, configure network access and security, and review maintenance settings. These choices affect how you connect and what you need to manage. AWS describes RDS as handling tasks such as backups, patching, monitoring and hardware provisioning, but you still make configuration and access decisions.
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When practice is complete, delete the instance and any associated resources you no longer need. Review the deletion choices carefully before confirming, especially if you want to retain a snapshot.
Project 2: Put Aurora and a web server in a VPC
Use AWS’s Aurora getting-started tutorial to deploy an Aurora cluster and a web server in a VPC. Exercise the application with a request that reads and writes data. This makes connectivity part of the lab: the application must reach the database through the configured network and security rules.
After the basic request works, extend the exercise with one operational task: restore a cluster from a snapshot, or use EventBridge to log a DB instance state change. These extensions help distinguish application behavior from database lifecycle operations.
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Project 3: Explore Aurora endpoints and operational changes
In a separate Aurora proof-of-concept, connect to the cluster endpoint for writes and data-definition language (DDL) operations, then use the reader endpoint for query-intensive sessions. Observe how the setup behaves when you adjust replicas or instance classes. The purpose is to learn how these options work, not to infer production capacity from a tutorial-sized deployment.
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AWS frames this kind of exercise as an evaluation against an intended use case. Treat any result as specific to the lab’s configuration and workload; it is not a capacity prediction for a production application.
Project 4: Build a small DynamoDB-backed tracker
Follow the DynamoDB getting-started guide to connect to the service, create a table and manage it. Then build a small tracker or catalog that stores and retrieves records through a supported access path. The application idea is a learning exercise; the guide’s core workflow is connecting to, creating and managing tables.
For local development and testing without accessing the DynamoDB web service, use DynamoDB Local. It provides a way to practice application interactions locally, while a hosted-service project teaches the managed-service workflow.
Check applicable AWS pricing before using the hosted service. AWS notes that standard usage charges can apply after any applicable free-tier benefits are exceeded. Delete unneeded tables and other resources after the exercise.
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Project 5: Add an ElastiCache layer to a read-heavy flow
Choose a documented ElastiCache learning path for Valkey, Redis OSS or Memcached, and begin with either a serverless cache or a designed cache cluster. Build a simple read-heavy application flow, then compare the path that reads from the cache with the path that reads from the persistent database.
The point is to see where a cache fits: AWS positions ElastiCache as an in-memory service for accelerating application and database performance. Keep the distinction clear in your design. A cache is not durable storage, so the application must be able to get authoritative data from the database when a cached value is unavailable.
Project 6: Demonstrate Aurora with ElastiCache
Once the services make sense individually, build a small relational-backed application with an ElastiCache layer. AWS documents a setup path for creating a cache using settings from an Aurora DB cluster in its Aurora integration guide.
Use the lab to make the data roles explicit: Aurora holds persistent relational data; the cache can serve selected reads that the application is able to retrieve again from Aurora. Check engine and Regional constraints before deploying. Do not treat cached data as the only copy of anything the application must retain.
Check availability, cost and cleanup before each lab
- Availability: Confirm the required service feature and engine version are supported in your chosen AWS Region. AWS’s Aurora cross-Region guidance illustrates that capabilities can vary by Region and version; check live service documentation for your specific deployment.
- Access: Hosted projects require an AWS account and appropriate permissions. Plan network access deliberately rather than assuming a database will be reachable from any client.
- Cost: AWS usage charges may apply. Review current pricing and any applicable free-tier terms before launching resources; a tutorial is not a promise that a deployment is free.
- Cleanup: After each exercise, remove resources you no longer need and check for related resources that may remain. For databases, decide deliberately whether to keep a snapshot before deletion.
What to build next
If you want another guided exercise, AWS’s Database Cookbook and the service tutorials offer further learning paths. Choose the next lab based on a specific skill—such as networking, table design or cache behavior—rather than combining services before you can explain why each one is there.
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