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4 Database Technologies for Large Scale Data

4 Database Technologies for Large Scale Data:

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.

Examples: Truviso

  • 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)

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