Crate is a fast, distributed SQL database that is incredibly easy to use and scale. Its unique, containerized, multi-model architecture gives SQL developers fast performance and the ability to process structured and unstructured data, without needing to be experts in back-end database administration and tuning.
Crate supports a variety of use cases, from a classic, scalable SQL database, to advanced, multi-model usage incorporating full-text search, geospatial, and analytics support.
Data is exploding: projections show that data production will be 44 times greater in 2020 than it was in 2009. This data explosion is an opportunity for companies to implement sophisticated analytics that discover new business opportunities and increase customer engagement. The problem is that traditional SQL stores are not designed for these data volumes and the NoSQL databases require a rewrite of existing logic and queries to process data. Crate offers the scalability and performance of a NoSQL database with a SQL layer, so you can use the same queries from your existing applications and direct integration from programming languages and frameworks.
Applications that require searching of full text documents or text heavy data have traditionally had to use a text search engine like Apache Lucene. For example: Find how many times a customer name is referenced in the email archive. A common problem companies like ClearVoice face with using text search engines is that they are complex to query because they use a propriety scripting languages, and they are separate from other data needed in the application. Crate's full text search provides powerful capabilities for analyzing, searching, sorting and querying text based on best in class technologies (like Lucene) with the productivity of a SQL interface.
Location data from mobile devices have increased the need for applications to store and process large sets of geospatial data and perform interesting analysis and queues of this data. For example: Analyzing traffic congestion patterns and proactively proposing alternative routing options. Geospatial queries on large data sets causes performance degradation, but Crate solves these issues by providing support for geospatial data types and functions that work across the data clusters using SQL syntax for increased productivity and ease of use.
Performing data intensive functions like ‘count’, ‘sum’, and ‘max’ becomes increasingly slow as data volumes grow. This is further complicated by analytical use cases where grouping by aggregation is a common requirement. For example: Finding the average temperature by region over the past 20 years. With Crate you can distribute the underlying data and functions and perform distributed aggregation across these clusters. Crate enables super fast analysis and measurement of data in real time using the SQL syntax you already know.