As the NoSQL market continues to shake out, MarkLogic has emerged as the only NoSQL database capable of taking on the database incumbents and winning for mission critical enterprise applications. In the last 30+ years, no one has been able to take on the incumbents and win on their own turf, so how are we doing it? Well, there is a lot to it, but here are the most important ways that MarkLogic is beating the incumbents:

1. Empowering the Enterprise
   MarkLogic solves a problem that every

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

Last week’s $100 million settlement between the state of Oregon and Oracle over a botched state health insurance website is both dismaying and illuminating.

It is dismaying because taxpayers are left holding the bag, once again, in terms of a huge loss while Oracle, initially hired to produce the site, ends up more entrenched in the state’s IT future.

The settlement is illuminating because it shows, once again, how enticing it is for entities, in this case Oregon, to stick with

Relational databases require ORM, which extracts the data away, tearing apart the data and adding more overhead in the process.

This post is the last one in my series about “why RDBMS aren’t working” to handle today’s data challenges. The previous posts focused on the relational data model being a poor fit for today’s data, and in this post I’m going show why the relational data model is a poor fit for modern app development.

Today, it’s a

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.