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3 posts tagged with "use-case"

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· 14 min read
Prashanth Rao

Ever since the birth of database management systems (DBMSs), tabular relations and graphs have been the core data structures used to model application data in two broad classes of systems: relational DBMSs (RDBMS) and graph DBMSs (GDBMS).

In this post, we'll look at how to transform data that might exist in a typical relational system to a graph and load it into a Kùzu database. The aim of this post and the next one is to showcase "graph thinking"1, where you explore connections in your existing structured data and apply it to potentially uncover new insights.

Code

The code to reproduce the workflow shown in this post can be found in the graphdb-demo repository. It uses Kùzu's Python API, but you are welcome to use the client API of your choice.

· 7 min read

IAMGraphViz Overview

Common Fate is a framework for managing complex cloud permissions. They provide tools to simplify access at scale to AWS, Azure, and Google Cloud accounts. You can learn about what you can do with Common Fate on their website. Here, we will talk about a recent proof of concept graph visualization tool called IAMGraphViz that Chang Liu (who is coauthoring this post) and I developed using Kùzu! IAMGraphViz is intended for infrastructure engineers to dig deep into the permission assignments in AWS IAM Identity Center using graph visualization. Using IAMGraphViz, one can easily visualize who has what type of access to different accounts on AWS as well as how they have access to these accounts. This is all done by analyzing the paths from users to accounts in a graph visualization, where the nodes and edges model users, accounts, groups, group memberships, permission sets and other entities in the AWS IAM Identity Center system.

· 13 min read
Semih Salihoğlu

In this post, we'll walk through how to use Kùzu as a Pytorch Geometric (PyG) Remote Backend to train a GNN model on very large graphs that do not fit on your machine's RAM.

Let's start with a quick overview of PyG Remote Backends: PyG Remote Backends are plug-in replacements for PyG's in-memory graph and feature stores, so they can be used seamlessly with the rest of the PyG interfaces to develop your GNN models. If a PyG Remote Backend is a disk-based storage system, such as Kùzu, PyG will fetch subgraphs from Kùzu, which stores and scans its data from disk, allowing you to train models on very large graphs for which PyG's in-memory storage would run out of memory and fail.