GraphDB

GraphDB

Ontotext
Java

Java

Oracle

About

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security. Proactively detect and investigate IT infrastructure using a layered security approach. Visualize all infrastructure to plan, predict and mitigate risk. Build graph queries for near-real-time identity fraud pattern detection in financial and purchase transactions.

About

*GraphDB allows you to link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs.* GraphDB is a highly efficient and robust graph database with RDF and SPARQL support. The GraphDB database supports a highly available replication cluster, which has been proven in a number of enterprise use cases that required resilience in data loading and query answering. If you need a quick overview of GraphDB or a download link to its latest releases, please visit the GraphDB product section. GraphDB uses RDF4J as a library, utilizing its APIs for storage and querying, as well as the support for a wide variety of query languages (e.g., SPARQL and SeRQL) and RDF syntaxes (e.g., RDF/XML, N3, Turtle).

About

The Java™ Programming Language is a general-purpose, concurrent, strongly typed, class-based object-oriented language. It is normally compiled to the bytecode instruction set and binary format defined in the Java Virtual Machine Specification. In the Java programming language, all source code is first written in plain text files ending with the .java extension. Those source files are then compiled into .class files by the javac compiler. A .class file does not contain code that is native to your processor; it instead contains bytecodes — the machine language of the Java Virtual Machine1 (Java VM). The java launcher tool then runs your application with an instance of the Java Virtual Machine.

About

Unlambda is a programming language. Nothing remarkable there. The originality of Unlambda is that it stands as the unexpected intersection of two marginal families of languages. Functional programming languages, of which the canonical representative is Scheme (a Lisp dialect). This means that the basic object manipulated by the language (and indeed the only one as far as Unlambda is concerned) is the function. Rather, Unlambda uses a functional approach to programming: the only form of objects it manipulates are functions. Each function takes a function as an argument and returns a function. Apart from a binary “apply” operation, Unlambda provides several built-in functions (the most important ones being the K and S combinators). User-defined functions can be created, but not saved or named, because Unlambda does not have any variables.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Any business or organization seeking a solution to build and run graph applications with highly connected datasets

Audience

Developers, Knowledge Managers, Solution Architects, Data Scientists

Audience

Developers looking for a Programming Language solution

Audience

Developers in need of an advanced Programming Language solution

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Amazon
Founded: 1994
United States
aws.amazon.com/neptune/

Company Information

Ontotext
Founded: 2008
Bulgaria
www.ontotext.com/product/graphdb

Company Information

Oracle
docs.oracle.com/javase/8/docs/technotes/guides/language/index.html

Company Information

Unlambda
www.madore.org/~david/programs/unlambda/

Alternatives

Alternatives

AnzoGraph DB

AnzoGraph DB

Cambridge Semantics

Alternatives

Alternatives

Racket

Racket

Racket Language
Apache TinkerPop

Apache TinkerPop

Apache Software Foundation
GraphDB

GraphDB

Ontotext
Dgraph

Dgraph

Hypermode
Zig

Zig

Zig Software Foundation

Categories

Categories

Categories

Categories

NoSQL Database Features

Auto-sharding
Automatic Database Replication
Data Model Flexibility
Deployment Flexibility
Dynamic Schemas
Integrated Caching
Multi-Model
Performance Management
Security Management

Big Data Features

Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates

Database Features

Backup and Recovery
Creation / Development
Data Migration
Data Replication
Data Search
Data Security
Database Conversion
Mobile Access
Monitoring
NOSQL
Performance Analysis
Queries
Relational Interface
Virtualization

Integrations

302.AI
Akto
ApiFlash
Augoor
Claude Opus 4.5
Claude Sonnet 3.5
Codecov
Coverity Static Analysis
DeepSeek R1
Grok 4
GroupBy
Magier
PDFreactor
Qwen2.5-1M
Realm
Snyk
SourceAI
Undrstnd
Workik
jBrowserDriver

Integrations

302.AI
Akto
ApiFlash
Augoor
Claude Opus 4.5
Claude Sonnet 3.5
Codecov
Coverity Static Analysis
DeepSeek R1
Grok 4
GroupBy
Magier
PDFreactor
Qwen2.5-1M
Realm
Snyk
SourceAI
Undrstnd
Workik
jBrowserDriver

Integrations

302.AI
Akto
ApiFlash
Augoor
Claude Opus 4.5
Claude Sonnet 3.5
Codecov
Coverity Static Analysis
DeepSeek R1
Grok 4
GroupBy
Magier
PDFreactor
Qwen2.5-1M
Realm
Snyk
SourceAI
Undrstnd
Workik
jBrowserDriver

Integrations

302.AI
Akto
ApiFlash
Augoor
Claude Opus 4.5
Claude Sonnet 3.5
Codecov
Coverity Static Analysis
DeepSeek R1
Grok 4
GroupBy
Magier
PDFreactor
Qwen2.5-1M
Realm
Snyk
SourceAI
Undrstnd
Workik
jBrowserDriver
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