Build a small order-data workflow with DynamoDB tables, customer-and-date queries and status indexes. You will protect writes from duplicates, handle expiring records and repair a lookup that misses recent orders.
The course contains five guided labs and one independent query-repair challenge. Each topic starts with a concrete access pattern and checks the resulting records.
What You Will Learn
- Create a table and read, write, update and delete typed items
- Query orders using partition and sort keys
- Add a global secondary index for order status
- Use conditional writes to prevent duplicate orders
- Configure TTL and exclude expired records from application reads
- Repair customer, date-range and pagination handling without mixing customer data
Who This Course Is For
This course is for AWS beginners and application developers who want key-based data access without first learning relational SQL.
Prerequisites: Complete Get Started with AWS on LabEx and the AWS Foundations labs on CLI commands, caller identity, resource names and output queries.
Learning environment: All activities run in a provided browser-based LabEx Linux environment. Use Terminal for AWS CLI commands and AWS View, next to Terminal, to inspect the same resource and application state. Tools and the connection are prepared; you do not need a personal AWS account or access keys. Each lab starts independently in a fresh VM.
Frequently Asked Questions
How should I choose keys and indexes?
Design the reads first. Orders for one customer in a date range are a key-based access pattern. S3 stores objects, while relational databases suit relational joins and different query patterns. Complex single-table design is outside this course.
Which capacity mode will I use?
The labs use on-demand capacity so you can create a table without selecting read/write capacity units. Provisioned capacity requires a planned request rate. Capacity planning, pricing and load testing are outside these exercises; see the AWS capacity guide.
Will every read immediately reflect a recent write?
A strongly consistent base-table read reflects successful writes before that read. Eventually consistent reads can lag, and global secondary indexes support eventual consistency. Repeat the read when observing an update; a read does not prevent later changes. See AWS read consistency.
Does TTL immediately remove expired records?
No. Expiration is asynchronous, so applications must exclude expired records that remain readable. AWS TTL.
What should I complete before the challenge?
Complete the table, query and index guided labs first. Global Tables, DAX, Streams and complex transactions are outside this course. Clean up owned resources while preserving unrelated reference data.
