Database Reference
In-Depth Information
picts a matrix that connects requirements from low storage/cost to high storage/cost
information management systems and analytics applications.
The following section lists all the capabilities that an integrated platform for Big Data
analytics should have:
• A data integration platform that can integrate data from any source, of any
type, and highly voluminous in nature. This includes efficient data extraction,
data cleansing, transformation, and loading capabilities.
• A data storage platform that can hold structured, unstructured, and semi-
structured data with a capability to slice and dice data to any degree, discard-
ing the format. In short, while we store data, we should be able to use the
best suited platform for a given data format (for example: structured data to
use relational store, semi-structured data to use NoSQL store, and unstruc-
tured data to use a file store) and still be able to join data across platforms to
run analytics.
• Supportforrunningstandardanalyticsfunctionsandstandardanalyticaltools
on data that has characteristics described previously.
• Modular and elastically scalable hardware that wouldn't force changes to ar-
chitecture/design with growing needs to handle bigger data and more com-
plex processing requirements.
• A centralized management and monitoring system.
• Highly available and fault tolerant platform that can repair itself in times of
any hardware failure seamlessly.
• Support for advanced visualizations to communicate insights in an effective
way.
• A collaboration platform that can help end users perform the functions of
loading, exploring, and visualizing data, and other workflow aspects as an
end-to-end process.
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