If you have skimmed through some of my other posts on this blog, it’s probably not surprising that I love using Neo4j in my projects. While you certainly can develop and work through your ideas locally, if you are like me, you probably have a few pet projects going at once, some of which you might want to share publicly.
This post aims to highlight how quickly you can get up and running using Cloud9, a cloud-based development environment.
Below is a quick writeup on how I use R and RNeo4j to munge my data and throw “larger” datasets into Neo4j. In short, I am fairly capable in R, so I prefer to use it to do the heavy lifting.
All I am doing is calling the neo4j-shell tool via ?system command. This post runs through how I have used this approach in some of my recent projects. I used this process for a project that I am currently working on at work, where 3+ million nodes and nearly 9 million relationships.
I have been watching the DiagrammeR package for a while now, and at this stage, it’s pretty impressive. I encourage you to take a look at what is possible, but be assured the framework is there to do some really awesome things.
One use-case that applies to me is that of data modeling an app within Neo4j. There are already some tools out there, namely:
Arrows Graphgen by GraphAware And you can always use graphgists The last link above is a sample graph gist that is a decent overview.
I have been playing with Neo4j quite a bit, mostly for fun as I learn how I figure out when and where I could apply it to solve various analytics problems. Neo4j, at it’s core, is a database, which allows us to query data in a structured way. While the graph model within Neo4j is very flexible, the cypher query language is fantastic. Once you get over the learning curve, with only a few lines of code you can do some really powerful queries.
I have been working on a team that is aiming to implement a Salesforce-based CRM solution for Enrollment Management. From the beginning, we had an aggressive timeline, and the project has taken many twists and turns along the way. While our experience is certainly not unique, and perhaps commonplace, it’s provided us with an opportunity to evaluate some of the fundamental steps that should be set in place prior to continuing down our deployment path considering our go-live date is currently TBD.
Intro The use of graphs to solve business problems is not new, as companies like Amazon, Netflix, and nearly all major social media sites have been doing this for some time. I have been obsessed with graphs for just as long, and after learning as much as I can about analysis of graphs and graph databases, I am finally getting the time to take what I have learned and apply it to real world data problems I face within Enrollment Management.