Hi graph gang,
In this week’s video, Will Lyon shows how to import spatial data for the GRANDstack Real Estate Search App.
Stefan Dreverman explains how to read and write data in the low code platform, Neo4j 4.1 is released, and Amr Khaled showed how to build an Instagram clone.
And finally, Matt Holford and Ravi Anthapu give us a crash course in modelling patient journeys in healthcare.
Featured Community Member: Ben Squire
This week’s featured community member is Ben Squire, Senior Data Scientist at Meredith Corporation.
Ben Squire – This Week’s Featured Community Member
He has been a Neo4j community member since 2018, and if you’ve ever googled a particularly mysterious problem you’ve had with Neo4j (especially if it involved lots of data!) you’ve probably come across his posts.
Ben spent last year working closely with our product engineering team as an early adopter for our Graph Data Science library. He ultimately used our graph algorithms to accomplish entity resolution at scale, but also provided invaluable feedback to help us make the library what it is today. He suggested useful features like seeding our community detection algorithms, and pushed the bounds of what was possible with memory and performance!
Ben has been a great advocate for graphs, and is always keen to share what he’s been working on. You can watch Identity Graph at Scale, a presentation from the Connections conference where he shared his experience doing Graph Data Science. He was also interviewed about his experience on ODBMS.org.
Building A GRANDstack Real Estate Search App: Part 2 | Data modeling and import
This week’s video is part of a series by Will Lyon showing how to build a GRANDstack Real Estate Search App.
In the second video, Will extends the graph data model to include geospatial information, and then uses APOC’s Load JSON procedure to import that data into Neo4j.
Modeling Patient Journeys with Neo4j
Matt Holford and Ravi Anthapu give us a crash course in modelling patient journeys in healthcare.
After explaining why graphs are uniquely positioned for this type of data, they iterate through different models, describing the use cases that each enable. In the second half of the post they show how to write queries to assess the performance of each model when answering different questions.
Building a low-code platform with Neo4j: Data
Stefan Dreverman continues his series of blog posts showing how to build a low-code platform with Neo4j.
In part 2, Stefan explains the way that we’re going to read and write data. He also shows outlines of the screens in the application and how they’ll interact with our data structures.
Neo4j 4.1 Released
Neo4j 4.1 was released this week and we’ve created content to help get you up to speed on the new features.
- Jennifer Reif created the Neo4j Graph Database developer guide, which acts as a useful starting point for discovering the features introduced in each release.
- Adam Cowley explains how to use the new Role Based Access Control features with the help of the Northwind Graph.
- I wrote an article showing how to manage the memory used by transactions,
- Michael Hunger gave us a whirlwind tour of 4.1 in his weekly live stream.
Microservices In Practice: Developing Instagram Clone —Graph Service
Amr Khaled has been writing a series of blog posts showing how to build an Instagram clone based on various microservices.
In part 6 Amr builds a follow system with help from Neo4j, the Spring Data Neo4j Library, and Cypher query language.
Tweet of the Week
My favourite tweet this week was by Roberto Rodriguez:
? Saturday project (WIP..)! ?— Roberto Rodriguez (@Cyb3rWard0g) June 20, 2020
a) Doc #Bloodhound cypher queries from the community in YAML
b) Auto parse queries & create #jupyter notebook to query a @neo4j DB via py2neo
c) Docker #jupyter server & #neo4j w/ #Bloodhound ExampleDB
d) Jupyter Book https://t.co/kD8zFKgcTU pic.twitter.com/62ASA7FSks
Don’t forget to RT if you liked it too!