Park Kieun (CUBRID Cluster Architect) gives an introduction to 4 large scale database technologies:
- Massively Parallel Processing (MPP) or parallel DBMS – A system that parallelizes the query execution of a DBMS, and splits queries and allocates them to multiple DBMS nodes in order to process massive amounts of data concurrently.
Examples: EBay DW, Yahoo! Everest Architecture, Greenplum, AsterData
- Column-oriented database – A system that stores the values in the same field as a column, as opposed to the conventional ow method that stores them as individual records.
Examples: Vertica, Sybase IQ, MonetDB
- Streaming processing (ESP or CEP) – A system that processes a constant data (or events) stream, or a concept in which the content of a database is continuously changing over time.
- Key-value storage (with MapReduce programming model) – A storage system that focuses on enhancing the performance when reading a single record by adopting the key-value data model, which is simpler than the relational data model.
Examples: many of the NoSQL databases covered here.
Even if I came up with the same 5 categories for scalable storage solutions, Park’s list is better documented. However we both left out distributed filesystems (sorry Jeff).
Original title and link: 4 Database Technologies for Large Scale Data (NoSQL databases © myNoSQL)