CrateDB is an open source, distributed database that combines SQL and search in a way that’s simple to scale. CrateDB 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 the variety and volume of the data involved, and the NoSQL databases require a rewrite of existing logic and queries to process data. CrateDB combines the scalability, performance, and agility of a NoSQL database with the ease of use and integration of SQL.
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 face with 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. CrateDB’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 has increased the need for applications to store and query large sets of geospatial data in interesting ways. For example: analyzing traffic congestion patterns and proactively proposing alternative routing options. CrateDB provides 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 CrateDB you can distribute the underlying data and functions and perform distributed aggregations across these clusters. CrateDB enables fast analysis and measurement of data in real time using the SQL syntax you already know.