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Benchmark Results – ArangoDB vs. Neo4j : ArangoDB up to 8x faster than Neo4j
Estimated reading time: 6 minutes
Introduction
This document presents the benchmark results comparing the ArangoDB’s Graph Analytics Engine (GAE) against Neo4j. The GAE is just one component of ArangoDB’s Data Science Suite.
This reproducible1 benchmark aims to provide a neutral and thorough comparison between the two databases, ensuring a fair and unbiased assessment.
We use the wiki-Talk dataset, a widely used, real-world graph dataset derived from the edit and discussion history of Wikipedia.
(more…)Vector Search in ArangoDB: Practical Insights and Hands-On Examples
Estimated reading time: 5 minutes
Vector search is gaining traction as a go-to tool for handling large, unstructured datasets like text, images, and audio. It works by comparing vector embeddings, numerical representations generated by machine learning models, to find items with similar properties. With the integration of Facebook’s FAISS library, ArangoDB brings scalable, high-performance vector search directly into its core, accessible via AQL (ArangoDB Query Language). Vector Search is now just another, fully-integrated data type/model in ArangoDB’s multi-model approach. The Vector Search capability is currently in Developer Preview and will be in production release in Q1, 2025.
This guide will walk you through setting up vector search, combining it with graph traversal for advanced use cases, and using tools like LangChain to power natural language queries that integrate Vector Search and GraphRAG.
(more…)Some Perspectives on HybridRAG in an ArangoDB World
Estimated reading time: 7 minutes
Introduction
Graph databases continue to gain momentum, thanks to their knack for handling intricate relationships and context. Developers and tech leaders are seeing the potential of pairing them with the creative strength of large language models (LLMs). This combination is opening the door to more precise, context-aware answers to natural language prompts. That’s where RAG comes in—it pulls in useful information, whether from raw text (VectorRAG) or a structured knowledge graph (GraphRAG), and feeds it into the LLM. The result? Smarter, more relevant responses that are grounded in actual data.
(more…)ArangoDB vs. Neo4J
Estimated reading time: 7 minutes
Advantages & Implications for Architects and Developers
ArangoDB and Neo4j are both powerful graph databases, but they have different strengths and features. Here’s a comparison showing how ArangoDB might be superior to Neo4j in certain scenarios, along with code snippets to illustrate:
(more…)ArangoDB 3.12 – Performance for all Your Data Models
Estimated reading time: 6 minutes
We are proud to announce the GA release of ArangoDB 3.12!
Congrats to the team and community for the latest ArangoDB release 3.12! ArangoDB 3.12 is focused on greatly improving performance and observability both for the core database and our search offering. In this blog post, we will go through some of the most important changes to ArangoDB and give you an idea of how this can be utilized in your products.
(more…)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).
(more…)Update: Evolving ArangoDB’s Licensing Model for a Sustainable Future
Estimated reading time: 7 minutes
Update: https://arangodb.com/2023/10/evolving-arangodbs-licensing-model-for-a-sustainable- future/
Last October the first iteration of this blog post explained an update to ArangoDB’s 10-year-old license model. Thank you for providing feedback and suggestions. As mentioned, we will always remain committed to our community and hence today, we are happy to announce yet another update that integrates your feedback.
(more…)The world is a graph: How Fix reimagines cloud security using a graph in ArangoDB
‘Guest Blog’
Estimated reading time: 5 minutes
In 2015, John Lambers, a Corporate Vice President and Security Fellow at Microsoft wrote “Defenders think in lists. Attackers think in graphs. As long as this is true, attackers win.ˮ
(more…)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.
(more…)Introducing ArangoDB’s Data Loader : Revolutionizing Your Data Migration Experience
Estimated reading time: 7 minutes
At ArangoDB, our commitment to empowering companies, developers, and data enthusiasts with cutting edge tools and resources remains unwavering. Today, we’re thrilled to unveil our latest innovation, the Data Loader, a game-changing feature designed to simplify and streamline the migration of relational databases to ArangoGraph. Let’s dive into what makes Data Loader a must-have tool for your data migration needs.
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