Apache TinkerPop

Apache TinkerPop

Apache Software Foundation
HyperGraphDB

HyperGraphDB

Kobrix Software

About

Apache TinkerPop™ is a graph computing framework for both graph databases (OLTP) and graph analytic systems (OLAP). Gremlin is the graph traversal language of Apache TinkerPop. Gremlin is a functional, data-flow language that enables users to succinctly express complex traversals on (or queries of) their application's property graph. Every Gremlin traversal is composed of a sequence of (potentially nested) steps. A graph is a structure composed of vertices and edges. Both vertices and edges can have an arbitrary number of key/value pairs called properties. Vertices denote discrete objects such as a person, a place, or an event. Edges denote relationships between vertices. For instance, a person may know another person, have been involved in an event, and/or have recently been at a particular place. If a user's domain is composed of a heterogeneous set of objects (vertices) that can be related to one another in a multitude of ways (edges).

About

HyperGraphDB is a general purpose, open-source data storage mechanism based on a powerful knowledge management formalism known as directed hypergraphs. While a persistent memory model designed mostly for knowledge management, AI and semantic web projects, it can also be used as an embedded object-oriented database for Java projects of all sizes. Or a graph database, or a (non-SQL) relational database. HyperGraphDB is a storage framework based on generalized hypergraphs as its underlying data model. The unit of storage is a tuple made up of 0 or more other tuples. Each such tuple is called an atom. One could think of the data model as relational where higher-order, n-ary relationships are allowed or as graph-oriented where edges can point to an arbitrary set of nodes and other edges. Each atom has an arbitrary, strongly-typed value associated with it. The type system managing those values is embedded as a hypergraph and customizable from the ground up.

About

StarTree, powered by Apache Pinot™, is a fully managed real-time analytics platform built for customer-facing applications that demand instant insights on the freshest data. Unlike traditional data warehouses or OLTP databases—optimized for back-office reporting or transactions—StarTree is engineered for real-time OLAP at true scale, meaning: - Data Volume: query performance sustained at petabyte scale - Ingest Rates: millions of events per second, continuously indexed for freshness - Concurrency: thousands to millions of simultaneous users served with sub-second latency With StarTree, businesses deliver always-fresh insights at interactive speed, enabling applications that personalize, monitor, and act in real time.

About

Simple, fast, safe, and compiled. For developing maintainable software. Simple language for building maintainable programs. You can learn the entire language by going through the documentation over a weekend, and in most cases, there's only one way to do something. This results in simple, readable, and maintainable code. This results in simple, readable, and maintainable code. Despite being simple, V gives a lot of power to the developer and can be used in pretty much every field, including systems programming, webdev, gamedev, GUI, mobile, science, embedded, tooling, etc. V is very similar to Go. If you know Go, you already know 80% of V. Bounds checking, No undefined values, no variable shadowing, immutable variables by default, immutable structs by default, option/result and mandatory error checks, sum types, generics, and immutable function args by default, mutable args have to be marked on call.

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

Developers seeking a solution to manage their graph databases and graph analytic systems

Audience

Anyone who needs an open-source data storage framework based on generalized hypergraphs

Audience

Companies with up to petabytes of data needing to run high queries-per-second (QPS) workloads

Audience

Developers interested in a language for building maintainable programs

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

Free
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

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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

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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

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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

Apache Software Foundation
United States
tinkerpop.apache.org

Company Information

Kobrix Software
Founded: 2015
United States
hypergraphdb.org

Company Information

StarTree
Founded: 2019
United States
www.startree.ai

Company Information

V Programming Language
United States
vlang.io

Alternatives

Alternatives

Graph Engine

Graph Engine

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Alternatives

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CelerData Cloud

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Alternatives

Grakn

Grakn

Grakn Labs
Swift

Swift

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GraphBase

GraphBase

FactNexus
Zig

Zig

Zig Software Foundation

Categories

Categories

Categories

Categories

Big Data Features

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

Data Analysis Features

Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics

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

Streaming Analytics Features

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards

Integrations

Amazon Kinesis
Amazon Redshift
Amazon S3
Apache Flink
Apache Kafka
Apache Pinot
C
Confluent
Delta Lake
Embeddable
Google Cloud BigQuery
Hadoop
Helix Editor
Java
JavaScript
Microsoft Azure
Node.js
Redpanda
Snowflake
Tableau

Integrations

Amazon Kinesis
Amazon Redshift
Amazon S3
Apache Flink
Apache Kafka
Apache Pinot
C
Confluent
Delta Lake
Embeddable
Google Cloud BigQuery
Hadoop
Helix Editor
Java
JavaScript
Microsoft Azure
Node.js
Redpanda
Snowflake
Tableau

Integrations

Amazon Kinesis
Amazon Redshift
Amazon S3
Apache Flink
Apache Kafka
Apache Pinot
C
Confluent
Delta Lake
Embeddable
Google Cloud BigQuery
Hadoop
Helix Editor
Java
JavaScript
Microsoft Azure
Node.js
Redpanda
Snowflake
Tableau

Integrations

Amazon Kinesis
Amazon Redshift
Amazon S3
Apache Flink
Apache Kafka
Apache Pinot
C
Confluent
Delta Lake
Embeddable
Google Cloud BigQuery
Hadoop
Helix Editor
Java
JavaScript
Microsoft Azure
Node.js
Redpanda
Snowflake
Tableau
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