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About GraphDB

GraphDB is a graph database software from Ontotext that supports knowledge graph management. It provides advanced reasoning, SPARQL querying, and data integration so users can effectively manage and retrieve complex data relationships. GraphDB is designed for handling large volumes of data while maintaining high performance. It supports both RDF and OWL, allowing for versatile data modeling and semantic capabilities. Additionally, it includes features for text search, versioning, and data visualization. Key capabilities: RDF data storage SPARQL query processing Reasoning support Text search integration Versioning control Best for: data scientists and developers that need to manage and analyze interconnected datasets.

GraphDB Details

Vendor
Ontotext
Year Launched
2008
Location
Europe Bulgaria 111R Tsarigradsko Shosse Synergy Tower, fl. 12 Sofia 1784 Bulgaria
Deployment
cloud, on premise, windows, linux
Training Options
demo, account manager, community
Countries Served
All Countries
Languages
English
Users
Engineering, Education & Learning
Industries Served
Healthcare, Education, Finance, Retail
Tags
Database Software, GraphDB

GraphDB's In-App Market Place

Does GraphDB have an in-app market place?

Yes

How many Mini-Apps in the marketplace?

1

Mini Apps

N/A

Pricing Options

Free trial
Free version
Request a quote
Promo Offer

Accepted Payment Currencies

USD ($), EUR (€), GBP (£), JPY (¥), AUD (A$), CAD (C$), CHF (Fr.), CNY (¥), SEK (kr), NOK (kr), DKK (kr), NZD (NZ$), SGD (S$), HKD (HK$), KRW (₩)

Pros & Cons

  • 1. Graph databases excel at modeling interconnected data like social networks.
  • 2. They are a perfect match for object-oriented system models.
  • 3. Changing the data model in a graph database is easier compared to relational databases.
  • 4. The software is easy to use and fast to deploy.
  • 1. Graph databases generally have a limited number of connectors.
  • 2. They are typically not suitable for network partitioning due to high interconnectivity.
  • 3. Graph databases often do not scale out effectively for very large datasets.
  • 4. Implementing versioning and auditing of data can be challenging in graph databases.

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