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Unmatched Contextual Relevance: Transform Your Data Interactions Beyond Mere Connections
"Global Relay’s number one challenge was how to scalably incorporate contextual relevance into its intuitive directory search. With this setup, when a participant’s name or job title changes and needs to be updated, we can do that with a single write – without propagating across all possible instances of the participant."
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Case Studies
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Graph Traversal Depth
With advanced Graph Traversal capabilities, ArangoDB can explore deeper relationships in data. In practical terms, a social media app could leverage this to analyze user interactions, friendship links, and content preferences to multiple levels, providing intricate insights and enabling the creation of highly personalized user experiences and content recommendations. These insights are far beyond what legacy systems with traversal limitations could achieve.
Unified Query Language
ArangoDB’s AQL allows complex querying across data models. A practical application could be a healthcare system using AQL to interlink patient records (document model) with relationships to healthcare providers and medical facilities (graph model). This provides nuanced insights into patient care pathways and enables more informed and contextually relevant healthcare decisions. This approach is far more streamlined compared to systems with multiple or rigid query languages.
Optimized Indexing Options
ArangoDB’s versatile Indexing Options, like Fulltext, aid efficient data retrieval. For instance, a research database could employ optimized indexing to swiftly retrieve academically relevant articles and publications based on keywords, allowing researchers to gain contextually relevant insights and references quicker and more accurately than with legacy systems having less flexible indexing options.
Scalable Data Distribution
The Horizontal Scalability and Sharding features in ArangoDB ensure effective data distribution. In the context of a large scale IoT application managing vast datasets from multiple devices, this feature enables seamless scaling to accommodate growing data volumes, ensuring uninterrupted and contextually relevant analyses of device interactions and data points, a task that is notably challenging for legacy systems lacking robust scalability options.
Multi-Model Data Integration
ArangoDB's Multi-Model Architecture uniquely integrates various data models. For example, an e-commerce platform can employ this feature to correlate customer data (document model) with purchasing behaviors and relationships (graph model) in a SINGLE query, offering a richer context to understand customer preferences and tailor marketing strategies, which would be cumbersome using legacy systems with single-model focuses.
ArangoDB vs. Legacy Graph DBs For
Contextual Relevance
| ArangoDB For Contextual Relevance | Legacy Graph Databases for Contextual Relevance | |
|---|---|---|
| Complex search-based relevance ranking |  |  |
| SmartGraphs for expedited context-rich traversals | | |
| Efficient edge filtering | | |
| Large-scale graph processing algorithms | | |
| Weighted graph traversal | | |
Get started free today
(no credit card required), and experience the shortest time to value for a hosted graph DB.
Read the
Case Studies
Learn why companies across industries are switching to ArangoDB for Graph.