DBMS > ArangoDB vs. Dgraph vs. Google BigQuery vs. Kinetica

System Properties Comparison ArangoDB vs. Dgraph vs. Google BigQuery vs. Kinetica

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Editorial information provided by DB-Engines

Name

Description

Native multi-model DBMS for graph, document, key/value and search. All in one engine and accessible with one query language.

Distributed and scalable native Graph DBMS

Large scale data warehouse service with append-only tables

Fully vectorized database across both GPUs and CPUs

Primary database model

Document store
Graph DBMS
Key-value store
Search engine

Graph DBMS

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	3.32
Rank	#90	Overall
	#15	Document stores
	#5	Graph DBMS
	#12	Key-value stores
	#10	Search engines

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Website

arangodb.com

dgraph.io

cloud.google.com/bigquery

www.kinetica.com

Technical documentation

docs.arangodb.com

dgraph.io/docs

cloud.google.com/bigquery/docs

docs.kinetica.com

Social network pages

Developer

ArangoDB Inc.

Dgraph Labs, Inc.

Google

Kinetica

Initial release

2012

2016

2010

2012

Current release

3.11.5, November 2023

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial license (Enterprise) available

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

ArangoDB Cloud –The Managed Cloud Service of ArangoDB. Provides fully managed, and monitored cluster deployments of any size, with enterprise-grade security. Get started for free and continue for as little as $0,21/hour.

Implementation language

C++

C, C++

Server operating systems

Linux
OS X
Windows

hosted

Linux

Data scheme

schema-free

automatically recognizes schema within a collection

schema-free

yes

Typing

predefined data types such as float or date

yes

string, double, boolean, list, hash

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

AQL
Foxx Framework
Graph API (Gremlin)
GraphQL query language
HTTP API
Java & SpringData
JSON style queries
VelocyPack/VelocyStream

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

Supported programming languages

C#
C++
Clojure
Elixir
Go
Java
JavaScript (Node.js)
PHP
Python
R
Rust

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

JavaScript

user defined functions

in JavaScript

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

since version 2.0

yes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with configurable replication factor

Synchronous replication via Raft

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

can be done with stored procedures in JavaScript

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

configurable per collection or per write
Immediate Consistency
OneShard (highly available, fault-tolerant deployment mode with ACID semantics)

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

relationships in graphs

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

GPU vRAM or System RAM

User concepts

Access control

yes

Planned for future releases

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles on table level

More information provided by the system vendor

Specific characteristics

Graph and Beyond. With more than 11,000 stargazers on GitHub, ArangoDB is the leading...
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Competitive advantages

Consolidation: As a native multi-model database, can be used as a full blown document...
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Typical application scenarios

Native multi-model in ArangoDB is being used for a broad range of projects across...
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Key customers

Cisco, Barclays, Refinitive, Siemens Mentor, Kabbage, Liaison, Douglas, MakeMyTrip,...
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Market metrics

ArangoDB is the leading native multi-model database with over 11,000 stargazers on...
» more

Licensing and pricing models

Very permissive Apache 2 License for Community Edition & commercial licenses are...
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