Amazon DynamoDB

Amazon DynamoDB is a fully managed, serverless NoSQL key-value and document database. It delivers single-digit millisecond latency at any scale, with built-in horizontal sharding, multi-AZ replication, and optional global replication — and no servers, patches, or storage capacity to manage.

Key Features:

• Serverless & Auto-Scaling: Two capacity modes — on-demand (scales instantly with traffic, pay per request) and provisioned (reserve RCUs/WCUs with optional auto-scaling).
• Single-Digit Millisecond Latency: Partitioned by hash key with SSD-backed storage; DAX provides microsecond read latency via an in-memory cache.
• Primary Keys, GSIs, LSIs: Partition key (+ optional sort key) defines the primary access pattern; Global Secondary Indexes enable alternative query patterns.
• DynamoDB Streams: Change-data-capture stream of item-level modifications, consumed by Lambda or Kinesis for event-driven architectures.
• Global Tables: Multi-region, active-active replication with last-writer-wins conflict resolution.
• Transactions: ACID transactions across up to 100 items or 4 MB for multi-item operations that must succeed or fail together.
• TTL: Automatic expiry of items based on a timestamp attribute — useful for sessions, caches, and compliance retention.
• PITR & On-Demand Backup: Point-in-time recovery for the last 35 days plus on-demand snapshots retained indefinitely.

Common Use Cases:

• High-Scale Web & Mobile Backends: Session stores, user profiles, shopping carts, leaderboards.
• IoT Telemetry: Millions of small writes per second with time-partitioned sort keys.
• Serverless Architectures: Natural backing store for Lambda + API Gateway applications.
• Gaming: Player state, matchmaking, and leaderboards with low latency globally.
• Event Sourcing: Append-only event logs consumed via Streams.

Data Modeling Principles:

• Design around access patterns first — unlike relational DBs, schema follows queries.
• Favor single-table design with composite keys and GSIs for item-collection queries.
• Keep partition keys high-cardinality to avoid hot partitions.
• Prefer batch (BatchGetItem, BatchWriteItem) and Query over Scan.

Example: PutItem and Query

import boto3
from boto3.dynamodb.conditions import Key

table = boto3.resource("dynamodb", region_name="us-west-2").Table("Orders")

table.put_item(Item={
    "pk": "CUSTOMER#1042",
    "sk": "ORDER#2026-04-21#A-482",
    "total": 129.95,
    "status": "SHIPPED",
})

resp = table.query(
    KeyConditionExpression=Key("pk").eq("CUSTOMER#1042") & Key("sk").begins_with("ORDER#2026-04"),
)
for item in resp["Items"]:
    print(item["sk"], item["status"], item["total"])
  

DynamoDB is the default choice on AWS when you need predictable performance at any scale and your access patterns can be modeled as key-value or document lookups.