Definition :
Big data is an extremely large data set that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.
Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate.
Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
characteristics:
Volume:
The quantity of generated and stored data.
Velocity:
The speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development.
Variety:
The type and nature of the data.
Variability:
Inconsistency of the data set can hamper processes to handle and manage it.
Veracity:
The quality of captured data can vary greatly, affecting accurate analysis.
Visualization:
The process of interpreting data in visual terms or of putting into visible form.
Value:
Transforming data into the regard that something is held to deserve, the importance, worth, or usefulness of something.
Validity :
data quality, governance, master data management (MDM) on massive, diverse, distributed, heterogeneous, “unclean” data collections.
Venue:
distributed, heterogeneous data from multiple platforms, from different owners’ systems, with different access and formatting requirements, private vs. public cloud.
Vocabulary :
schema, data models, semantics, ontologies, taxonomies, and other content- and context-based metadata that describe the data’s structure, syntax, content, and provenance.
Vagueness:
confusion over the meaning of big data
Big data is an extremely large data set that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.
Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate.
Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
characteristics:
Volume:
The quantity of generated and stored data.
Velocity:
The speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development.
Variety:
The type and nature of the data.
Variability:
Inconsistency of the data set can hamper processes to handle and manage it.
Veracity:
The quality of captured data can vary greatly, affecting accurate analysis.
Visualization:
The process of interpreting data in visual terms or of putting into visible form.
Value:
Transforming data into the regard that something is held to deserve, the importance, worth, or usefulness of something.
Validity :
data quality, governance, master data management (MDM) on massive, diverse, distributed, heterogeneous, “unclean” data collections.
Venue:
distributed, heterogeneous data from multiple platforms, from different owners’ systems, with different access and formatting requirements, private vs. public cloud.
Vocabulary :
schema, data models, semantics, ontologies, taxonomies, and other content- and context-based metadata that describe the data’s structure, syntax, content, and provenance.
Vagueness:
confusion over the meaning of big data
