Teradata Cloud and Big Data at Netflix

Posted on: October 15th, 2014 by sgnau No Comments

 

I’ve said to many people over the last few months that there are misconceptions about the intersection of cloud and analytics.  Our position is simple: cloud deployment for analytic solutions should be simple, integrated, dependable, and about the geography.  True analytics can’t thrive in the cloud if limitations—like reduced functionality, performance or availability are introduced.         Read More…

 

It’s a pleasure to accept Scott’s invitation to post here, in conjunction with the joint announcement by Teradata and Cloudera of our deep new business and technical relationship. Our companies have been driving the state of the art in data management separately — Teradata for three and a half decades, Cloudera for six and a         Read More…

Industry Recognition for Best of Breed Excellence

Posted on: September 10th, 2014 by sgnau No Comments

 

Teradata customers have always seen how hard we work to deliver the data architectures they need today and simultaneously innovate for what they’re going to need tomorrow.  But it’s certainly great to see outside recognition on these fronts as well.  As summer draws to a close, I remain humbled and honored by the kudos we         Read More…

The Biggest Piece of the Big Data Puzzle

Posted on: September 3rd, 2014 by sgnau No Comments

 

Getting the most out of your big data projects is an important – although often complex – goal. As I’ve written before, a data warehouse is one key piece of the puzzle … but successful big data projects increasingly require the integration of open source platforms like Hadoop and NoSQL as well. That’s why I         Read More…

 

I wrote recently about how Teradata has embraced open source technologies in multiple ways over the years. Of course, Hadoop is the technology that at once remains open source’s biggest prospect and toughest nut to crack in terms of reaping value from data at the enterprise-level. So I think it’s worth some extra discussion. Hadoop’s         Read More…

Open Embrace of Open Source

Posted on: August 26th, 2014 by sgnau 4 Comments

 

It’s been nice to see industry coverage in Information Week, Business-Cloud.com and elsewhere pick up on how June’s release of Teradata Aster-R reflects our company’s strong dedication to making open source technologies more enterprise-ready. Aster-R integrates the popular R statistical and data visualization open source language into Teradata systems, and it follows last year’s partnership         Read More…

Helping the R Guru Democratize Data

Posted on: June 27th, 2014 by sgnau No Comments

 

One of the more important dialogues in analytics lately has been around R programmers and how this corps of technology professionals is getting more access to more data in order to reap more business value. R is, of course, the free software environment for statistical computing and graphics widely used by data scientists, statisticians and         Read More…

 

I often mention the best-of-breed vision needed for liquid analytics and how seemingly different technologies should work seamlessly to complete the analytics ecosystem for customers. You’ve also probably heard me talk about connectors and Teradata’s recently-announced QueryGrid to operationalize the liquid vision through coordinated queries across multiple, complex resources and analytic options. Now I’m proud         Read More…

Putting Hadoop to Work in the Enterprise

Posted on: June 5th, 2014 by sgnau No Comments

 

I have always been a big fan of Hadoop and its potential for analytics. Even as we’ve seen Hadoop endure some growing pains on the way to maturity, Teradata has remained committed to finding strategic ways to deploy and manage Hadoop within enterprise architectures. That’s why Hadoop has figured so fully into our best of         Read More…

Liquid Analytics Gets Real for Enterprises

Posted on: April 7th, 2014 by sgnau No Comments

 

Last week I wrote how our industry is evolving toward a “liquid” approach to analytics where queries can be dispatched across one cohesive, interconnected and complementary architecture involving bi-directional movement of data to analytics and/or analytics to data. I used the analogy of an automobile – easy to drive despite tons of complexity under the         Read More…