Datomic is a proprietary database management system. It is an operational DBMS, in other words, it allows updates in real time. Instead of assigning and overwriting values into named attributes, Datomic keeps track of all immutable facts over time, which sets Datomic apart because previous states can be accessed easily. Datomic is also a distributional DBMS, which provides horizontal read scalability. Therefore, Datomic is a good fit for storing a history of valuable records that requires operational flexibility and read scalability. In addition, Datomic Cloud is built for AWS integration and is a quicker and easier way to access Datomic on AWS.
Early March 2012, the Relevance team (later joined with Metadata to form Cognitect) around Rich Hickey released Datomic, which they have started to work on since 2010. To make better use of recent advancements in cloud computing, Datomic Cloud was release in early 2018, which is better tailored to AWS.
Datomic treats storage as a service, which means that Datomic only provides the ways to access underlying storage, but doesn't provide the actual storage. Storage services could also be switched easily by simply [changing the connection string](https://docs.datomic.com/on-prem/storage.html#storage-services).
Stored procedures are represented as [transaction functions](https://docs.datomic.com/on-prem/database-functions.html#transaction-functions) in Datomic, which can be invoked during transaction processes.
Datomic indexes are covering indexes. In other words, instead of storing reference to data in the index, Datomic directly reads data from index. The index trees are shallow, with at most 3 levels: root, directories and segment leaf. Datomic maintains four index trees with different sorting orders for efficient access of different queries.
Datomic stores immutable facts as datoms over time. A datom follows the form of `(entity, attribute, value, time).` Although Datomic doesn't require a table schema that specifies attribute columns in advance, it requires to specify properties of individual attributes. This is called universal schema.
Index trees contain "segments," arrays of records that are serialized and then compressed with zip. Compression allows more efficient caching and storage.
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