At the time, he naturally knew that it would become a favorite metaphor used the world over to describe whenever we didn’t have a lot of something and then suddenly we had a lot of that same thing in great diversity. In fact, I’m sure this is precisely what he was thinking. The database ecosystem suddenly brimmed with diversity due to the Cambrian explosion of NoSQL technologies, making it impossible to cover the whole space in one blog post. This series will focus on the largest category within the NoSQL space: aggregate stores. Aggregate stores include key-value stores, wide-column stores, and document stores, which are distinguished from graph databases by their lack of connections between aggregates. Key-value stores store values organized by identifiers known as keys, with a basic model consisting of buckets that replicate onto several machines for fault-tolerance reasons. Wide-column stores have a sparsely populated table whose rows can contain arbitrary columns, providing natural indexing through keys. Document stores store and retrieve documents like an electronic filing cabinet, allowing for natural hierarchies and hierarchical JSON documents. While aggregate stores are good at storing big sets of discrete data, they sacrifice a data model, language, and functionality for handling data relationships, making them less suitable for problems that require understanding connectedness. Graph database technology, on the other hand, embraces relationships to solve problems involving context and connectedness.