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What is Amazon DynamoDB?

Understand DynamoDB tables, keys, access-pattern design, consistency, indexes, transactions, streams, capacity modes, and hot partitions.

What is Amazon DynamoDB?

Amazon DynamoDB is a fully managed, serverless NoSQL database for key-value and document data. It distributes data by primary key, supports low-latency access at scale, and provides on-demand or provisioned capacity, secondary indexes, transactions, change streams, backups, and multi-Region global tables.

How this topic fits

Data

Select object, NoSQL, relational, and streaming data services from access patterns, consistency, scale, and recovery requirements.

Model from access patterns, not entities alone

Start by listing the reads and writes the application must perform, including key conditions, sort order, result size, consistency, and expected traffic. Design partition and sort keys so those operations use targeted key access instead of broad scans.

DynamoDB does not perform relational joins for the application. Related records can be stored together under a partition key when that improves a known access pattern, while duplicated projections must have a deliberate update strategy.

Keys define distribution and query capability

A simple primary key has one partition-key attribute. A composite primary key adds a sort key, allowing several ordered items under the same partition key. Global and local secondary indexes expose additional query patterns.

Indexes consume storage and write capacity and are separate consistency and backfill considerations. Add one because a named access pattern requires it, not as a substitute for understanding the table model.

Choose consistency and write controls explicitly

Reads can be eventually consistent by default, with strongly consistent reads available for supported operations and resources. Conditional writes protect invariants such as uniqueness, version checks, and idempotency without a read-before-write race.

Transactions group multiple supported reads or writes when the business rule truly spans items. They add cost and constraints, so use them for correctness boundaries rather than recreating relational behavior everywhere.

Prevent hot keys and operate data lifecycle

Traffic must distribute across partition-key values. One extremely active key can become a bottleneck even when aggregate table capacity is sufficient; write sharding, caching, aggregation changes, or a different boundary may be required.

Choose on-demand or provisioned capacity from traffic knowledge and cost goals. Enable point-in-time recovery where required, control encryption and IAM access, watch throttling and consumed capacity, and test restoration rather than only enabling backups.

Go deeper with AWS

Read the primary AWS documentation behind the service definitions and architecture guidance in this guide.

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