Amazon DynamoDB is a managed NoSQL service with strong consistency and predictable performance that shields users from the complexities of manual setup.
Whether or not you’ve actually used a NoSQL data store yourself, it’s probably a good idea to make sure you fully understand the key design differences between NoSQL (including Amazon DynamoDB) and the more traditional relational database (or “SQL”) systems like MySQL.
First of all, NoSQL does not stand for “Not SQL“, but “Not Only SQL“. The two are not opposites, but complementary. NoSQL designs deliver faster data operations and can seem more intuitive, while not necessarily adhering to the ACID (atomicity, consistency, isolation, and durability) properties of a relational database.
There are many well-known NoSQL databases available, including MongoDB, Cassandra, HBase, Redis, Amazon DynamoDB, and Riak. Each of these was built for a specific range of uses and will therefore offer different features. We could group these databases into the following categories: columnar (Cassandra, HBase), key-value store (DynamoDB, Riak), document-store (MongoDB, CouchDB), and graph (Neo4j, OrientDB).
In this post, I’m going to focus on Amazon DynamoDB — the giant of the NoSQL world. I believe it’s become a giant because AWS built it for their own operations. Considering how much was at stake financially, anything less than complete reliability would simply not be tolerated. Software created in such a demanding environment and with the use of AWS-scale resources is bound to be epic. The result? Fantastic reliability and durability, with blazing fast service.
Like any other AWS product, Amazon DynamoDB was designed for failure (i.e., it has self-recovery and resilience built in). That makes DynamoDB a highly available, scalable, and distributed data store. Here are ten key features that helped make Amazon DynamoDB into a giant.
1. Amazon DynamoDB is a managed, NoSQL database service
With a managed service, users only interact with the

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