Big Data, Analytics, and Data Science Life Cycle - Getting Started with Greenplum for Big Data Analytics - page 13

• Initially, it was always Reporting . Data was pre-processed and loaded in
batches, and an understanding of "what happened?" was gathered.
• Focus slowly shifted on to understanding "why did it happen?". This is with
the advent of increased ad-hoc data inclusion.
• At the next level, the focus has shifted to identifying "why will it happen?", a
focus more on prediction instead of pure analysis.
• With more ad-hoc data availability, the focus is shifted onto "what is happen-
ing?" part of the business.
• Final focus is on "make it happen!" with the advent of real-time event access.

With this paradigm shift, the expectations from a new or modern data warehousing
system have changed and the following table lists the expected features:

Traditional analytics ap-
proach

New analytics ap-
proach Challenges

Scalability N Y

Ingest high volumes of data N Y

Data sampling Y N

Data variety support N Y

Parallel data and query process-
ing

N Y

Quicker access to information N Y