* Scalable - Crate is horizontally scalable, meaning that you can grow it by adding machines, without a need to re-shuffle, re-index or move data around. * Distributed SQL & Real-time search - Crate’s distributed SQL query engine takes a standard SQL query, deconstructs it and executes it in a distributed fashion on the crate cluster (think mapreduce in real time). * Highly available - Loosing disks, a node, or even multiple nodes in a Crate cluster are automatically repaired. * Real-time ingestion - Most analytic workloads get ingested in batch loads, often with transactional locks and other overhead. * Any data and BLOBs - Crate´s columnar store supports both relational data, as well as nested json-documents, where all nested attributes can be included in any SQL command. * Timeseries - Since Crate allows the (automatic) creation of partitions (virtual tables) of any table, it effectively gives you separate partitions under the hood, with high performance. * Geospatial - Store and query geographical information of many kinds using the geo_point and geo_shape types. * Dynamic schemas - Contrary to many other scale-out solutions Crate´s schemas are totally flexible. * Transactional - Crate is eventually consistent but offers transactional semantics.

* Domain Model Flexibility - Shape domain models in a most suitable way, responding to needs and challenges as they come using built-in indexing capabilities * Perfect Audit Trail - Every event and its cause are serialized so that every action and its outcome can be traced * Functionality Mapping - Designating commands and events provides developers with a clear, succinct "one-page view" of available functionality * Strongly Typed Schema - All commands and events are compactly serialized according to their typing information and schema migration capabilities are included * Tooling - The Java implementation comes with JMX introspection and management primitives built-in. Additional application tooling is available, such as a GraphQL adaptor