ALL COVERED TOPICS

NoSQL Benchmarks NoSQL use cases NoSQL Videos NoSQL Hybrid Solutions NoSQL Presentations Big Data Hadoop MapReduce Pig Hive Flume Oozie Sqoop HDFS ZooKeeper Cascading Cascalog BigTable Cassandra HBase Hypertable Couchbase CouchDB MongoDB OrientDB RavenDB Jackrabbit Terrastore Amazon DynamoDB Redis Riak Project Voldemort Tokyo Cabinet Kyoto Cabinet memcached Amazon SimpleDB Datomic MemcacheDB M/DB GT.M Amazon Dynamo Dynomite Mnesia Yahoo! PNUTS/Sherpa Neo4j InfoGrid Sones GraphDB InfiniteGraph AllegroGraph MarkLogic Clustrix CouchDB Case Studies MongoDB Case Studies NoSQL at Adobe NoSQL at Facebook NoSQL at Twitter

NAVIGATE MAIN CATEGORIES

Close

Big Data: How about the other V's?

Doug Laney (Gartner):

Many vendors and pundits have attempted to augment Gartner’s original “3Vs” from the late 1990s with clever(?) “V”s of their own. However, the 3Vs were intended to define the proportional dimensions and challenges specific to big data. Other “V”s like veracity, validity, value, viability, etc. are aspirational qualities of all data, not definitional qualities of big data. Conflating inherent aspects with important objectives leads to poor prioritization and planning.

The same way aspirational qualities can be detrimental to prioritization and planning (and I agree with this), the same way a definition that tries to also be a guidelines and best practice is confusing1.


  1. Let’s not forget that the V-based definition of Big Data is not a mathematical definition that would require very rigorous, and strict, terms. 

Original title and link: Big Data: How about the other V’s? (NoSQL database©myNoSQL)

via: http://blogs.gartner.com/doug-laney/batman-on-big-data/