Adam Ronthal has worked in the technology industry for over 17 years in technical operations, system administration, and data warehousing and analytics. In 2006, Adam joined Netezza as a Technical Account Manager, working with some of IBM Netezza's largest data warehousing and analytic customers and helping them architect and implement their Netezza-based solutions. Adam led the team to write the Netezza NZLaunch Handbook, a practical implementation guide for IBM Netezza customers, and served as editor of the final guide. Today, Adam works in technical marketing for the IBM Netezza brand. Adam is an IBM Certified Specialist for Netezza, and holds a BA from Yale University.
Sr. Technical Marketing Analyst, IBM
March 27, 2012
In this post, I will explore the most important of the golden rules of appliances - appliances are easy to use. For my discussions of the first two rules, see my previous posts appliances are Plug and Play and appliances are purpose-built.
March 5, 2012
I’ve been working through my golden rules of appliances in my previous posts, and evaluating various vendor’s appliance claims as we go. Appliances are Plug and Play Appliances are purpose-built Appliances are easy to use
March 1, 2012
My first encounter with in-memory database technology was back in 1998 when I was working at e-commerce pioneer Open Market. At the time, we had acquired an in-memory database solution that powered an online product catalog. Because everything was in-memory, the response time as different parameters were applied to product searches was excellent -- and even more than a decade ago, sufficient memory was available at reasonable cost to enable this kind of operational type workload.
February 13, 2012
In my previous post, I defined a framework by which to evaluate the appliance claims of data warehouse and analytic vendors. These truisms, which I have come to refer to as the golden rules of appliances are:
February 6, 2012
The term "appliance" is liberally used by many vendors in the big data space these days. It seems that almost everyone has latched onto the term and it is being used not only to define data warehousing and analytic product offerings, but also to subtly (or not so subtly in some cases) set customer expectations about the underlying ease of deployment and ongoing cost of ownership associated with the product.