Advanced Fraud Detection in Financial Services with ArangoDB and AQL

Estimated reading time: 3 minutes

Advanced Fraud Detection: ArangoDB’s AQL vs. Traditional RDBMS

In the realm of financial services, where fraud detection is both critical and complex, the choice of database and query language can impact the efficiency and effectiveness of fraud detection systems. Let’s explore how ArangoDB – a multi-model graph database – is powered by AQL (ArangoDB Query Language) to handle multiple, real-world fraud detection scenarios in a much more seamless and powerful way compared to traditional Relational Database Management Systems (RDBMS).

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Reintroducing the ArangoDB-RDF Adapter

Estimated reading time: 1 minute

ArangoRDF allows you to export Graphs from ArangoDB into RDFLib, the standard library for working with Resource Description Framework (RDF) in Python, and vice-versa.

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What is a Graph Database?

Estimated reading time: 10 minutes

Introduction

Graphs occur everywhere in everyday life: your network of friends, the network of roads you drive on, and the supply chain of factories, ships, and roads that brought you the device you’re reading this on. While it might be easy to connect the dots on how most things can be shown as a graph, what makes a database a graph database? That is the question you will have the answer to in this blog post, but to put it simply: a graph consists of nodes, edges, and properties representing the relationships within data.

In this article, we will discuss:

  • What is a graph?
  • What is a graph database?
  • Different types of graph databases.
  • Graph database use cases.
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Data Science Personas banner

Who’s Who in Data Science

Estimated reading time: 10 minutes

Multiple data science personas participate in the daily operations of data logistics and intelligent business applications. Management and employees need to understand the big picture of data science to maximize collaboration efforts for these operations. This article will highlight the specialized roles and skillsets needed for the different data science tasks and the best tools to empower data-driven teams. You will come away from this article with a better understanding of how to support your own data science teams, and it is valuable for both managers and team members alike.

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