This Week in Neo4j: Data Fabric, Mapping App Flaws, Changes in Cypher, Graph Data Science, Recommendations, Visualization, CloudQuery, and More

This week’s newsletter features several interesting articles. Get started using graph visualization to execute security queries with “How to use CloudQuery for Attack Surface Management and Graph Visualization”, by Jason Kao blogging for the data integration platform CloudQuery. You’ll extract the data from AWS, then load it into Neo4j to create and build your own queries and relationships. Or try Gal Engelbert’s blog, “How to Automatically Map Application Flaws to MITRE ATT&CK Techniques and D3FEND Countermeasures”, demonstrating how a federated knowledge database created with Neo4j Data Fabric and NeoSemantics uncovers potential attack patterns and techniques as well as potential countermeasures.

There’s plenty more within!

Yolande Poirier

P.S.: If you’re a developer building modern applications with GraphQL, don’t forget to take this short, two-minute survey. We want to hear from you!

Vlad Batushkov is the Frontend Tech Lead and Engineering Manager at Agoda, an online travel platform, and a contributing writer for the Neo4j blog. In his NODES 2022 presentation, he described the creation of Graphville, an educational platform for beginners to learn Cypher and Neo4j basics. This indie project has been in development for three years and has become a public product. He looks back to the beginning of the project and summarizes the most important aspects. Watch his talk “Creating Graphville Neo4j Educational Platform”! Connect with him on LinkedIn.

FABRIC: How to Automatically Map Application Flaws to MITRE ATT&CK Techniques and D3FEND Countermeasures
Gal Engelberg presents an ontology-driven data federation architecture. It groups discovered application flaws by attack techniques and corresponding countermeasures.
NEO4J 5: Changes in Cypher
Follow the code with Tomaz Bratanic as he explains the new Cypher syntax on a dataset he cleaned and uploaded to a GitHub repository. He covers the new inline filtering options, and new syntax for defining unique constraints, existential subqueries and count subqueries.
NODES SESSION: Fundamentals of Neo4j Graph Data Science Series 2.x – Pipelines and More

Mats Rydberg shows you how to manage and transform your graphs, how to use machine learning pipelines, and how to make the best use of your trained models – all with a focus on the dedicated GDS Python Client, which enables the data scientist to remain in a familiar environment without losing the strength of the Neo4j graph database in the backend.

GRAPH VISUALISATION: How to use CloudQuery for Attack Surface Management

Create and build your own Attack Surface Management and Graph Visualization queries and relationships in Neo4j. Jason Kao demonstrates how to set up CloudQuery for customizable Attack Surface Management (ASM) and how to get started with utilizing graph visualization to execute security queries.

HANDS-ON: Recommendation System Using Neo4j

In this blog, Susmit explains how to create a recommendation system with Cypher queries. You’ll explore collaborative and content-based filtering and design the appropriate data model incrementally.

APP DEVELOPMENT: Mapping Neo4j OGM Query Results to DTOs & Records

In this video, Sebastian Daschner explains how to map query results to DTOs and/or Java records to simplify your code and make the query results more expressive.

INTERVIEW: Why Graph Query Language Matters

In this video interview with Sage Elliot, Jason Koo explains the differences between graph and relational databases, and discusses common use cases.

TWEET OF THE WEEK: @tlarsendataguy

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