Chapter 6. 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 insight. Initially, data warehousing used business intelligence tools to investigate historical data owned by the enterprise. This was 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 idea of bringing the analytics to the data rather than the data to the analytics because moving large datasets between systems is inefficient and costly. Greenplum formed an internal team of experienced practitioners in data science and developed analytic tools to work inside 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 has moved 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 has driven this growth. Interest is understandably high in many industries about 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 explosion of data, ...
Get Data Warehousing with Greenplum, 2nd Edition now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.