金皇朝|2

                              July 19, 2016

                              Notes on vendor lock-in

                              Vendor lock-in is an important subject. Everybody knows that. But few of us realize just how complicated the subject is, nor how riddled it is with paradoxes. Truth be told, I wasn’t fully aware either. But when I set out to write this post, I found that it just kept growing longer.

                              1. The most basic form of lock-in is:

                              2. Enterprise vendor standardization is closely associated with lock-in. The core idea is that you have a mandate or strong bias toward having different apps run over the same platforms, because:

                              3. That last point is double-edged; you have more power over suppliers to whom you give more business, but they also have more power over you. The upshot is often an ELA (Enterprise License Agreement), which commonly works:

                              Read more

                              December 12, 2014

                              Notes and links, December 12, 2014

                              1. A couple years ago I wrote skeptically about integrating predictive modeling and business intelligence. I’m less skeptical now.

                              For starters:

                              I’ve also heard a couple of ideas about how predictive modeling can support BI. One is via my client Omer Trajman, whose startup ScalingData is still semi-stealthy, but says they’re “working at the intersection of big data and IT operations”. The idea goes something like this:

                              Makes sense to me. (Edit: ScalingData subsequently launched, under the name Rocana.)

                              * The word “cluster” could have been used here in a couple of different ways, so I decided to avoid it altogether.

                              Finally, I’m hearing a variety of “smart ETL/data preparation” and “we recommend what columns you should join” stories. I don’t know how much machine learning there’s been in those to date, but it’s usually at least on the roadmap to make the systems (yet) smarter in the future. The end benefit is usually to facilitate BI.

                              2. Discussion of graph DBMS can get confusing. For example: Read more

                              July 5, 2012

                              Introduction to Neo Technology and Neo4j

                              I’ve been talking some with the Neo Technology/Neo4j guys, including Emil Eifrem (CEO/cofounder), Johan Svensson (CTO/cofounder), and Philip Rathle (Senior Director of Products). Basics include:

                              Numbers and historical facts include:

                              Read more

                              May 4, 2012

                              Notes on graph data management

                              This post is part of a series on managing and analyzing graph data. Posts to date include:

                              Interest in graph data models keeps increasing. But it’s tough to discuss them with any generality, because “graph data model” encompasses so many different things. Indeed, just as all data structures can be mapped to relational ones, it is also the case that all data structures can be mapped to graphs.

                              Formally, a graph is a collection of (node, edge, node) triples. In the simplest case, the edge has no properties other than existence or maybe direction, and the triple can be reduced to a (node, node) pair, unordered or ordered as the case may be. It is common, however, for edges to encapsulate additional properties, the canonical examples of which are:

                              Many of the graph examples I can think of fit into four groups: Read more

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