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Resolve and Unify Your Data: ArangoDB's Precision in Entity Resolution
"We needed a document db - as well as graph - that allowed us to easily query the data by effective date. Storing and processing data from more than 30k airports and airlines is a challenge itself but for our needs, it is necessary to store each modification of those entities with its own effective date as a document as well."
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Case Studies
Learn why companies across industries are switching to ArangoDB for Graph.
Real-Time Identity Matching
ArangoDB excels in instantaneous entity recognition across multiple data sets. This dramatically shortens response times for various operational aspects such as customer service and fraud detection. These real-time capabilities are critical and demonstrate computing efficiency required to handle such tasks swiftly.
Scalable Disambiguation
One of the key advantages of ArangoDB's Entity Resolution is its inherent scalability. The unique option to horizontally scale without losing transactions is critical to maintaining the high-speed processing and accuracy needed for complex entity resolution tasks. This is particularly useful for enterprises dealing with expanding data sets.
Enhanced Contextual Analysis
ArangoDB provides the ability to understand the context around data points, not just the data itself. By being able to infer relationships and associations among these points, it delivers a nuanced, multidimensional view of data. This kind of in-depth, contextual understanding is critical to getting a much deeper and more complete analytic picture of graph-based data.
Seamless Data Integration
ArangoDB's Entity Resolution feature offers unparalleled abilities to unify fragmented data from disparate sources. This goes beyond the capabilities of many legacy systems to create a consolidated, unified view. This not only streamlines analytics but also significantly improves the quality of data available for decision-making.
Reduced Operational Costs
ArangoDB's Entity Resolution simplifies the often complex and resource-intensive tasks of data cleaning and entity linking. By automating these processes and making them more accurate, it effectively lowers the total cost of ownership, making it a financially sound choice compared to older, less efficient systems.
ArangoDB vs. Legacy Graph DBs For Entity Resolution
| ArangoDB For Enterprise Knowledge Graphs | Legacy Graph Databases for Enterprise Knowledge Graphs |
|
|---|---|---|
| Real-Time Indexing |  |  |
| AQL (ArangoDB Unified Query Language) | | |
| Advanced Graph Traversal | | |
| Horizontal Scalability | | |
| Multi-Model Architecture | | |
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 about how companies across industries are switching to ArangoDB for Graph.