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Will Your Artificial Intelligence Project Set Back Your Career?
Posted by David Kaaret on 30 August 2018 08:00 AM

If your Artificial Intelligence (AI) project is bogged down with a wasteful, time-consuming and expensive data-preparation strategy, it could put your career at risk. Here is how you can anticipate and optimize your data-access needs to more effectively determine AI project outcomes, while avoiding potential technology pitfalls.

Anticipating Data-Access Needs for Your AI Project

Artificial Intelligence is a broad topic that encompasses natural language processing, speech and facial recognition,


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So reduzieren Multi Model Datenbanken die Datensilos
Posted by Stefan Rudo on 15 December 2016 06:47 PM

Kürzlich hatte ich eine Diskussion mit einigen Architekten, die für eines der größten Krankenversicherungs-Systeme in den USA arbeiten. Sie interessierten sich für die Aggregation ihrer unzähligen Silos mit medizinischen Daten (Gesundheitsdaten) und wollten diese Integration durch die Umwandlung der Daten in über 100 Milliarden semantische Tripel erreichen. Hier unser Vorschlag:

Als ich mit dem Projekt begann, wollten die Architekten des Unternehmens semantische Tripel verwenden, um die


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Relational Databases Are Not Designed For Mixed Workloads
Posted by Matt Allen on 13 November 2015 07:00 AM

Relational databases are designed for either OLTP or OLAP workloads. You can’t use one database for both. Today, that limitation is no longer acceptable as IT struggles to keep pace with the speed of business.

To recap, in previous posts I discussed two aspects of how relational databases have inflexible data models that are not designed for change, are not designed to handle data variety, and are not designed for scale. In this post, I am going to discuss another problem that has


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Relational Databases Are Not Designed For Scale
Posted by Matt Allen on 09 November 2015 07:00 AM

Relational databases are designed to run on a single server in order to maintain the integrity of the table mappings and avoid the problems of distributed computing.

We’re at a tipping point with data volume. In my last post, I showed the stat from EMC about how the digital universe is expected to grow from 4.4 zettabytes in 2013 to 44 zettabytes in 2020 (remember a zettabyte is 1 trillion gigabytes). That’s hockey stick growth, and we’re just at the start of the curve. Organizations


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Relational Databases Are Not Designed For Heterogeneous Data
Posted by Matt Allen on 01 November 2015 08:00 AM

Relational databases have resulted in accidental complexity that keeps most organizations spinning in circles. Organizations simply cannot keep up with the many shapes, sizes, and types data that are quickly growing in volume and changing.

In the previous post, I discussed why today’s dynamic, constantly changing data is a problem for relational databases. In this post, I am going to discuss a somewhat related, but unique problem that is also not easy for relational databases to handle.


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