ReBeerGraph: importing the Belgian BeerGraph straight from a Wikipedia HTML page

I have written about beer a few times, also on this blog. I have figured out a variety of ways to import The Wikipedia Page with all the belgian beers into Neo4j over the years. Starting with a spreadsheet based approach, then importing it from a Google Sheet (with it's great automated .csv export facilities), and then building on that functionality to automatically import the Wikipedia page into a spreadsheet using some funky IMPORTHTML() functions in Google sheets.

But: all of the above have started to crumble recently. The Wikipedia page, is actually kind of a difficult thing to parse automatically, as it splits up the dataset into many different HTML tables (which makes me need to import multiple datasets, really), and then it also seems like Wikipedia has added an additional column to it's data (the "Timeframe" or "Period" in which a beer had been brewn), which had lots of missing, and therefore empty cells. All of that messes up the IMPORTHTML() Google sheet that I had been using to automatically import the page into a gsheet.

So: I had been on the lookout for a different way of doing the automated import. And recently, while I was working on another side project (of course), I actually bumped into it. That's what I want to cover and demonstrate here: importing the data, directly from the page, without intermediate steps, automatically, using the apoc.load.html functionality.

Securing my Beergraph with Neo4j 4.0

Not sure if you have realised, but Neo4j has actually recently made the 4.0 version of the most fantastically awesome graph database on the planet available. You can get it ahead of the big launch event (on February 4th, 2020 - in case you were wondering!) from the Download Center and take it for a spin.

In this unbelievable release, there are so many new features, it's kind of hard to keep track of everything. But the ones that I can most easily get my head around are clearly

1. multi-database support - finally, Neo4j actually has this concept of running multiple databases on one database server. A multi-tenancy solution, that has been requested and anticipated by many of our users and customers. 
2. a VERY advanced schema-based security module, that allows people to extend the existing role-based security model of Neo4j even further - and make it crazy powerful. We'll spend a lot of time on that in this blogpost.

Readers of this blog probably know that I am a big fan of getting my feet down and dirty with our products, so this evening - with a couple of hours to spare, so to speak - I decided to try out the shiny new release. I spun up my Neo4j Desktop, and started reading some manual pages where stuff was explained. Specifically, I loved

• the manual pages on managing multiple databases
• the pages on authentication and authorisation
• a great video by Louise Söderström from Neo4j engineering:

Soon after flipping through this, I was on my way.

Graphs are blooming - again!

A few weeks ago, I wrote a two part blog post about Neo4j Bloom and how I was playing around with my BeerGraph and figuring out some cool features of the new Neo4j product. You can find these posts over here

• part 1
• part 2

It included a first little demo video,

that seems to have been liked by a bunch of people :) ... thanks for that.

Part 2/2: Graphs are Bloom-ing

Earlier I wrote about how I connected the newly announced preview version of Neo4j Bloom to my good old faithful Belgian BeerGraph. See part 1 of this 2-part series for that story. I actually split up the story into two parts, because I feel like there's a super interesting and powerful part to Bloom that deserves a bit more attention: the mechanism of the custom Search Phrases.

As we mentioned in the previous post, Bloom structures your exploration and discovery into specific "views" on the graph data, called "Perspectives. You can select the perspective you find most appropriate from a dropdown - and customize/tweak/create perspectives yourself if you are not happy with the auto-generated starting point.

Part 1/2: Graphs are Bloom-ing

Last week something happened that really excited me. We, Neo4j, finally announced Bloom and demonstrated our own Graph Visualisation and Discovery tool, Neo4j Bloom. This is a technology that we have long been pondering, have experimented with in a number of ways, and have long looked to find and develop an offering that would be interesting and differentiated in what is already a very well looked-after marketplace. 

I am not exaggerating when I say that is truly exciting. Not only do many of our customers want to be able to visualise the results of their graph queries, but the graph data model is also unique in the way that it provides such an intuitive, easy to understand data model that lends itself so well to a GRAPH-ical representation. It truly fits into the Graph Platform vision that Neo4j has been advocating since 2017.