This Week in Neo4j: Recommendation, Needle, Microservices, Data Import, Data Analysis, and More

Welcome to this week’s roundup! Read on for info about upcoming free trainings, as well as several good articles. Try “What is Graph Data Science?” for a good point of departure to integrate graph data science techniques into your data science team. Another suggestion is “Ingesting Big Data into Neo4j” to transform a 350 GB compressed JSONL file to CSV. In the “Of Interest” section try setting up an IOT system to display data on noxious gases from multiple sensors – no soldering required!

Yolande Poirier
PS: The new series of technical workshops starts next week with the Intro to Neo4j on March 15!


Véronique Gendner has worked in data analysis, data processing, graphs, web applications, and more for many years. She is currently in the research field, building web applications for data processing and information display. Connect with her on LinkedIn.

In her NODES 2022 presentation, “Genealogy With Different Graph Technologies for Data Collection and Visualization,” she takes genealogical research as an example to illustrate possibilities that can be applied to many research domains. She demonstrates several graph issues and technologies, from data collection to visualization. Watch her talk!

RECOMMENDATION: Building a Simple Movie Recommender With Python and Neo4j
Dimitris Panagopoulos explains how to use a graph database to create a simple movie recommendation system. He demonstrates finding similar movies using Cypher, suggesting movies to users, and calculating user similarity. You can reproduce the same setup, since all code and data is hosted on GitHub.
Costa Alexoglou explains how Neo4j design and engineering teams confronted UI/UX inconsistencies, and created a strategy for operationalizing and scaling design. He describes the process that created the current design system dedicated to building and maintaining the design library, the code library, and the guidelines website.
NODES SESSION: Divide and Conquer: Send Forth the Microservices

Jennifer explores how microservices divide functionality and responsibility and multiply their forces to handle load and complexity. Starting with introductory application chatter and working our way to a fully operational pipeline with microservices architecture, we’ll go from Level 1 Sorcerer to Level 20.

TUTORIAL: A Complete Introduction to Critical New Ways of Analyzing Your Data

Sean Robinson explains how integrating graph data science into the data science workflow provides new methods to extract information from your data that would otherwise be lost. He lists commonly used tools that provide data structures, functions, and models. Also, he summarizes techniques of exploratory data analysis for graphs and offers guidance on which graph algorithms to use in various situations.

DATA IMPORT: Ingesting Big Data Into Neo4j

Check out this series of blogs by Ebru Cucen and Fahran Wallace on working with graph data, in which they ingest 400 million nodes and a billion relationships into Neo4j. While part one of the series models and extracts data, this post describes data processing from json to csv. An upcoming part three ingests that CSV file using the neo4j-admin import tool.

TWEET OF THE WEEK: @graphtft

Don’t forget to retweet, if you like it!
… Of Special Interest

  • Storing BME680 Sensor data on Neo4j Graph Database and visualizing it on Docker Extension. Here’s a project that shows how to fetch sensor values, push to Neo4j Graph database, and display using the Neo4j Docker Extension. Check it out!
  • New Steering for Neo4j Drivers? Richard Macaskill, Product Manager at Neo4j, explains how the drivers team has been working on simplifying the experience of new users getting started with Neo4j. Check it out!
  • An Ensemble Chatbot for Healthcare. Can crowdsourcing chatbots produce more accurate results? Check it out!