This is part two of a series covering core DynamoDB concepts, from philosophy all the way to single-table design. The goal is to get you to understand idiomatic usage and trade-offs in under an hour.
Today, we're looking at the DynamoDB data model – what the main abstractions are, what you can do with them,  and how they scale.

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DynamoDB tables are conceptually hash tables of B-trees, where partition keys map to item collections and sort keys order items within collections. Any access not based on partition and sort keys requires expensive full table scans. Physical partitions have hard throughput limits (1 MB/s writes, 24 MB/s eventually consistent reads) and automatically split based on size or traffic. Items are limited to 400 KB, and query responses to 1 MB pages. Secondary indexes (global and local) enable alternative access patterns. The system supports eventual and strong consistency, conditional writes, transactions up to 100 items, batch operations, and change data capture via streams.

DynamoDB crash course: part 2 – data model