Tag Archives: data warehousing

Implementing Teradata Temporal in a Physical Data Model using ERwin

June 15, 2016

There is no question that a database-supported temporal implementation gives Teradata users a powerful tool, because the time dimension has been added to data management and query processing. For the business user, the ability to ask more sophisticated time-based questions from their data warehouse, and receive more insightful answers, can yield a distinct competitive advantage.… Read More »

Let’s Be Clear: DevOps and the Agile Approach

March 2, 2016

If you are a curious individual then, like me, when you hear a new buzzword of occupational interest, you Google it and try to understand what it really means and how it fits into what you do. Right? More important, you want to know how you can apply this to your everyday work life to… Read More »

Data Modeling Requires Detailed Mapping — Learn Why

November 30, 2015

Mapping is an important step to understanding your data and where the data resides in your ecosystem. Mapping takes us from the known to the unknown and is effectively accomplished by using mapping tools, adopting best practices, and having a common understanding of how the mappings will be used. But mapping does take a considerable amount… Read More »

Simplifying SAP R/3 is irrelevant for users

October 14, 2015

Part two of a series about an old ‘SAP’ dog who learns a new trick Today more than ever, SAP is more focused on technology (HANA) than data. When they do focus on data, they talk about simplifying it because simplification is necessary to make said technology work better. In SAP terms, simplification means fewer… Read More »

Pluralism and Secularity In a Big Data Ecosystem

August 25, 2015

Solutions around today’s analytic ecosystem are too technically driven without focusing on business values. The buzzwords seem to over-compensate the reality of implementation and cost of ownership. I challenge you to view your analytic architecture using pluralism and secularity. Without such a view of this world your resume will fill out nicely but your business… Read More »

Optimization in Data Modeling 1 – Primary Index Selection

July 14, 2015

In my last blog I spoke about the decisions that must be made when transforming an Industry Data Model (iDM) from Logical Data Model (LDM) to an implementable Physical Data Model (PDM). However, being able to generate DDL (Data Definition Language) that will run on a Teradata platform is not enough – you also want… Read More »

Agile Data Warehousing Meets Agile Technology

April 20, 2015

By Youko Watari, Technical Marketing Manager Agile development and its 12 principles[1] have been adopted everywhere, and data warehousing is not an exception. The Agile data warehousing publications out there mostly focus on how to adopt Agile tools and techniques such as the backlog, user stories, and iterations into the data warehouse development process, or how… Read More »

Harness Cross-Functional Centrality of Data Analytics for Competitive Advantage – Part 1 of 2

April 7, 2015

High Level Data Analytics Graph (Healthcare Example)  <—- Click on image to view GRAPH ANIMATION Michael Porter, in an excellent article in the November 2014 issue of the Harvard Business Review[1], points out that smart connected products are broadening competitive boundaries to encompass related products that meet a broader underlying need. Porter elaborates that the boundary shift… Read More »

Hybrid Row-Column Stores: A General and Flexible Approach

March 10, 2015

During a recent meeting with a post-doc in my lab at Yale, he reminded me that this summer will mark the 10-year anniversary of the publication of C-Store in VLDB 2005. C-Store was by no means the first ever column-store database system (the column-store idea has been around since the 70s — nearly as long… Read More »

Data-Driven Design: Smart Modeling in the Fast Lane

February 24, 2015

In this blog, I would like to discuss a different way of modeling data regardless of the method such as Third Normal Form or Dimensional or Analytical datasets. This new way of data modeling will cut down the development cycles by avoiding rework, be agile, and produce higher quality solutions. It’s a discipline that looks… Read More »