This Week in Neo4j: OSS Dependencies, Workspace, LLM, Streaming Graph Data, and More

Welcome to the newsletter! This week, Michael Hunger graphs open-source software dependencies, an important use case within software analytics for graph databases. See the video below and read the linked blog “Analyzing Software Dependencies With” for more information.

Also in the news: Neo4j Workspace received an update which you can read about via Gregory King’s blog.  New features include Saved Cypher, plan view in text format, file import pre-filter, and import cancel. Check out these and other changes to Workspace in Aura Free and Professional.

Yolande Poirier


Zach Probst is a technologist passionate about cybersecurity. He loves to make tools that help developers worry less about doing things in a secure fashion, and get more time to build cool things! You can follow him on LinkedIn.

In his NODES 2022 presentation, he dives into the architecture of Intuit’s system and the decisions behind it. Additionally, he shows some real examples of just how easy it is to develop new ingestion pipelines. Watch his talk!

Neo4j DEVTOOLS: Better in Neo4j Workspace
Gregory King summarizes recent updates to the Neo4j Workspace. There’s Saved Cypher, previously known as Favorites in the Browser, which offers export and import in CSV format. Query now has a single :param command (though :params is aliased to :param for convenience) and sports improved graph visualisation. And now, you can stop an import job once it’s started.
DATASET: Creating a Web of Synthetic Data
Jason Koo introduces the Mock Data Generator, a collection of tools to generate synthetic graph data. You’ll use ChatGPT to convert text into a graph, modify it visually with, then automatically create the nodes and relationships.
DISCOVERING Neo4j AURADB: Analyzing Software Dependencies With

In this episode of Discover AuraDB Free, Michael Hunger creates a graph of open-source software packages using the API, which provides access to Open Source Insights, a software package service developed and hosted by Google. An accompanying blog is also available.

LLMs: Harnessing Large Language Models With Neo4j

Oskar Hane describes a project integrating of graph database technology and concepts into an LLM application stack. Two use cases are covered: developing a natural language interface for knowledge graphs and creating knowledge graphs from unstructured data sources, including PDFs, HTML pages, and text documents.

CLOUD: Streaming Graph Data With Confluent Cloud and Neo4j on Google Cloud

Ezhil Vendhan and Elena Cuevas explore a streaming data architecture with Confluent Cloud, a managed service for Apache Kafka. In this example, Neo4j Aura and Confluent Cloud provide a streaming architecture that can extract value from connected data.

TWEET OF THE WEEK: @adamcowley

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