This Week: Knowledge Graph, Data Loading, Olympics, CSV

Welcome to This Week in Neo4j, your weekly fix for news from the world of graph databases!
In this week’s edition, we learn how to use Mix and Batch for improved data loading. We also look back at the Olympics 2024 from a data perspective through graph analysis, convert a CSV file into a graph and see what many people think about getting better results from LLMs with Knowledge Graphs.

Have you registered for NODES 2024 yet? Soon, we kick off Road to NODES with Neo4j Fundamentals, Graph App Development on Aura, GraphViz and GraphRAG as hands-on workshops. Register your spot today!

I hope you enjoy this issue,
Alexander Erdl

COMING UP NEXT WEEK!

Conferences: Find us at Gartner IT Symposium, AU, The AI Conference, US & Javaforum Nord, DE on September 09-10, CDAO Financial Services, UK & Data Expo, NL on September 10-11
Meetup: Meet us in Vienna, AT on September 18
NODES 2024: Register Now! for November 07
All Neo4j Events: Webinars and More
GraphSummit Series: Get Connected With Graphs – Next Stop: San Francisco on September 25

FEATURED COMMUNITY MEMBER: Martin Jurran

Martin is a software engineer passionate about digital transformation, business analysis, and complex projects, driven by impact, valuing feedback, and a love for continuous learning.
Connect with him on LinkedIn.

Understanding key concepts and benefits of graph databases is very important. Therefore, Martin recently wrote “Everything You Need to Know About Graph Databases & Neo4j”, explaining why graph databases are a powerful tool for developers and architects dealing with connected data. He highlights the advantages of graph databases in handling complex relationships and outlines Neo4j’s architectural goals, including ease of use, performance, reliability, and scalability.

KNOWLEDGE GRAPH: Knowledge Graphs are Making LLMs Less Dumb

Knowledge graphs help reduce AI hallucinations, provide up-to-date information, and use the relationships between data points to enhance the quality of AI-generated content. Sagar Sharma summarises this in his blog post with many quotes from important and influential voices from the space.

DATA LOADING: Mix and Batch: A Technique for Fast, Parallel Relationship Loading in Neo4j

When Neo4j writes a relationship, it must lock the source and destination nodes. If more than one thread tries to write to the same node simultaneously, the second thread must wait until the lock frees up. Eric Monk gives a detailed step-by-step guide on using the Mix and Batch technique. He was able to make the load go two to three times faster.

OLYMPICS 2024: Graph Analysis: Paris 2024 Olympics

Alex Edwards shares his graph analysis of the 2024 Olympics, which revealed hidden patterns and connections between nations and sports, highlighting the power of graph theory in uncovering strategic insights and guiding decisions in complex networks.

CSV: Converting CSV Data to a Neo4j Graph Database To RAG system

Prince Krampah showcases in this video a project where he took the Northwind Traders Sales Dataset in CSV format and converted this dataset into a Neo4j graph database to explore the power of graph databases for data analysis.