Chapter 5. Gaining Analytic Insight
It’s all well and good to efficiently organize and load data, but the purpose of an analytic data warehouse is to gain such insights using a wide variety of tools. Initially, data warehousing used business intelligence tools to investigate previous history of the enterprise. These were embodied in Key Performance Indicators (KPIs) and some limited “What-If” scenarios. Greenplum was more ambitious. It wanted users to be able to do predictive analytics and process optimization using data in the warehouse. To that end, it employed the model of bringing the analytics to the data rather than the data to the analytics. To assist customers, Greenplum formed an internal team of experienced experts in data science and developed analytic tools to work within Greenplum. This chapter explores the ways in which users can gain analytic insight using the Pivotal Greenplum Database.
Data Science on Greenplum with Apache MADlib
What Is Data Science and Why Is It Important?
Data science is moving with gusto to the enterprise. The potential for business value in the form of better products and customer experiences as well as mounting competitive pressures is driving this growth. Interest is understandably high in many industries on how to build and run the appropriate predictive analytics models on the pertinent data to realize this business value.
Much of the interest is due to the proliferation of data generated by the Internet ...
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