Database Reference
In-Depth Information
High performance continues to be a critical success indicator for user implement-
ations in
Data Warehousing
(
DW
),
Business Intelligence
(
BI
),
Data Integration
(
DI
), and analytics. Advanced analytics includes techniques such as predictive ana-
lytics, data mining, statistics, and
Natural Language Processing
(
NLP
).
A few important drivers for analytics are listed as follows:
• Need to optimize business operations/processes
• Proactively identify business risks
• Predict new business opportunities
• Compliance to regulations
Big Data analytics is all about application of these advanced analytic techniques to
very large, diverse data sets that are often multi-structured in nature. Traditional data
warehousing tools do not support the unstructured data sources and the expecta-
tions on the processing speeds for Big Data analytics. As a result, a new class of Big
Data technology has emerged and is being used in many Big Data analytics environ-
ments. There are both open source and commercial offerings in the market for this
requirement.
The focus of this topic will be Greenplum UAP that includes database (for structured
data requirements), HD/Hadoop (for unstructured data requirements), and Chorus (a
collaboration platform that can integrate with partner BI, analytics, and visualization
tools gluing the communication between the required stakeholders).
The following diagram depicts the evolution of analytics, very clearly, with the in-
crease in data volumes; a linear increase in sophistication of insights is sought.
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