DynamoDB TTL and Access Pattern Lab

Goal

Reinforce Partition keys, On-demand capacity, TTL, Access patterns through a small, inspectable activity. This is a learning exercise, not a production design: use a dedicated sandbox or test account, record what you observe, and do not copy the permissions or data-handling choices into production without review.

What you should be able to explain afterward: what AWS managed for you, what you configured, what evidence proves the result, and what could continue to cost money or expose data if you leave it behind.

Concepts Reinforced

• Partition keys
• On-demand capacity
• TTL
• Access patterns

Prerequisites

• AWS CLI configured
• Permission to create DynamoDB tables

Before you start

1. Choose one AWS Region and write it down; many resources and console views are Region-specific.
2. Confirm that you are operating only in an authorized non-production account and that you can identify the account ID before creating anything.
3. Open the AWS service documentation linked below if a console label or command option is unfamiliar. The lab is successful when you understand the observation, not when you click through it quickly.

Estimated Cost

Exact Steps

Read the entire sequence before beginning. Substitute your own test names for every placeholder, keep the Region consistent, and pause after each step to inspect the result. If a command returns an error, do not repeatedly retry it without reading the error: check Region, account, permissions, resource identifiers, and whether the preceding step actually completed.

1. Create a small on-demand table with a partition key named pk.
2. Enable TTL on an attribute named expiresAt.
3. Put one item with a future Unix timestamp and one without TTL.
4. Query or get the items by key and observe that TTL is not an immediate delete mechanism.

aws dynamodb create-table --table-name DJamesStudyTtl --attribute-definitions AttributeName=pk,AttributeType=S --key-schema AttributeName=pk,KeyType=HASH --billing-mode PAY_PER_REQUEST

Verification

Verification is the evidence that the concept worked. Capture the relevant ARN, status, identity, log entry, policy result, object version, or query output before cleanup. A successful command alone is not proof that the intended control behaved correctly.

• describe-time-to-live shows ENABLED.
• The table can retrieve the item by partition key.

If the result is different

• Confirm the selected Region and AWS account.
• Check the exact resource identifier and current status.
• Review the service event history, CloudTrail event, CloudWatch log, or command output when available.
• Re-read the relevant IAM policy, trust policy, security rule, or service setting instead of assuming the service is at fault.

Cleanup Steps

Cleanup is part of the lab. Remove test resources in dependency order, delete temporary credentials or local files, and check the billing or resource console for anything that remains. Some services retain versions, snapshots, logs, or recovery artifacts even after the visible parent resource is deleted.

• Delete the table.

Sources and Review Metadata