Teradata Just Became Operations-Ready on Hortonworks

Posted on: December 17th, 2014 by Cesar Rojas No Comments

 

The partnership with one of our three strategic Hadoop partners, Hortonworks, continues to evolve. Today Teradata has gained the “Operations Ready Certification,” a new Hortonworks certification that validates our focus and strategy about providing an enterprise Hadoop experience to our Teradata customers. Teradata Viewpoint has become a standard interface for monitoring and management of the Teradata         Read More…

Graph Processing Inside an Analytic DBMS

Posted on: December 16th, 2014 by Daniel Abadi No Comments

 

Although the Bulk Synchronous Parallel (BSP) model for scalable parallel processing was invented by Leslie Valiant in the 1980s (and was cited as part of the reason for Valiant’s recent Turing award), it became a popular model for scalable processing of graph data in 2010 when Grzegorz Malewicz et. al. from Google published their seminal         Read More…

 

A recent blog – “Why use an Industry Data Model?” – discussed how industry data models bring exceptional business value to customers by providing a logical “blueprint” for data integration and enterprise analytics within well-defined business spaces. If this data model is expanded to include a template industry physical data model (iPDM), can the model         Read More…

 

A series on ‘what’s next in analytics on SAP R/3’ – part 4 Engineers are people that want to fix a problem! As you may recall in my first Blog I wrote about Arno Luijten, one of the senior engineers in my team, who almost 10 years ago proposed data replication as the fix for         Read More…

 

Where is the business world today in relation to the long-term effort to incorporate insights from big data? Winston Churchill put it best: “We are at the end of the beginning.” In the Silicon Valley ecosystem, Hadoop has been the rage for several years, but the larger business world is just starting to come to         Read More…

Why Use an Industry Data Model? Speed Time to Your Objectives

Posted on: December 1st, 2014 by Guest Blogger No Comments

 

I have been involved in the development of Industry data models since the late ’90s and still to this day I get asked “what’s the value of using an Industry Data Model?” For anyone unfamiliar with data models, here is the definition from Wikipedia: “Data models are often used as an aid to communication between         Read More…

Integrate Data, Processes & People: End Data Ownership Turf Wars

Posted on: November 18th, 2014 by Guest Blogger No Comments

 

The biggest thing I’ve realized over the past couple of months, other than Tony Romo is one of the best NFL players I’ve ever seen — is the fact that data ownership is still a huge problem for companies of all types. Tony’s numbers keep getting more impressive game by game – and likewise the         Read More…

Data Lake Best and Worst Practices

Posted on: November 10th, 2014 by Dan Graham No Comments

 

Data Lake Best Practices Where’s the Money? Rule #1: start with a data lake justification owned and signed off on by the business leaders. Regardless of how inexpensive a data lake may seem, it still costs money. OK, so what is the value of the data lake? Focus as much energy as possible on getting         Read More…

Optimizing Disk IO and Memory for Big Data Vector Analysis

Posted on: November 10th, 2014 by Daniel Abadi No Comments

 

At Yale, every spring I usually teach an advanced database systems implementation class that covers both traditional and more modern database system architectures. I often like to test my students with questions like the following: Let’s say the following SQL query is issued to a data warehouse for a retail store (the query requests the         Read More…

Teradata QueryGrid and Adaptive Optimization

Posted on: October 17th, 2014 by Daniel Abadi No Comments

 

Not Your Typical DBMS-Hadoop Connector For those readers who followed my writings for the Hadapt blog before it was acquired by Teradata http://hadapt.com/blog/, one of my common refrains was the architectural flaws inherent in database connectors to Hadoop. My problems with connectors centered on the following issues: (1) Big data may mean different things to         Read More…