DBMS > Google BigQuery vs. GridGain vs. Neo4j vs. OrientDB

System Properties Comparison Google BigQuery vs. GridGain vs. Neo4j vs. OrientDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

GridGain is an in-memory computing platform, built on Apache Ignite

Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Graph DBMS

Document store
Graph DBMS
Key-value store

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	44.47
Rank	#23	Overall
	#1	Graph DBMS

Score	3.27
Rank	#97	Overall
	#16	Document stores
	#6	Graph DBMS
	#15	Key-value stores

Website

cloud.google.com/bigquery

www.gridgain.com

neo4j.com

orientdb.org

Technical documentation

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

neo4j.com/docs

www.orientdb.com/docs/last/index.html

Developer

Google

GridGain Systems, Inc.

Neo4j, Inc.

OrientDB LTD; CallidusCloud; SAP

Initial release

2010

2007

2010

Current release

GridGain 8.5.1

5.19, April 2024

3.2.29, March 2024

License

Commercial or Open Source

commercial

Open Source

GPL version3, commercial licenses available

Open Source

Apache version 2

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.

Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.

Implementation language

Java, C++, .Net

Java, Scala

Java

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux

Can also be used server-less as embedded Java database.
OS X
Solaris
Windows

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

schema-free and schema-optional

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

pluggable indexing subsystem, by default Apache Lucene

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language, no joins

APIs and other access methods

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

yes

User defined Procedures and Functions

Java, Javascript

Triggers

yes (cache interceptors and events)

yes

via event handler

Hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes using Neo4j Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Causal Clustering using Raft protocol

available in in Enterprise Version only

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

relationship in graphs

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

Access rights for users and roles; record level security configurable

More information provided by the system vendor

Specific characteristics

Neo4j delivers graph technology that has been battle tested for performance and scale...
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Competitive advantages

Neo4j is the market leader, graph database category creator, and the most widely...
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Typical application scenarios

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Key customers

Over 800 commercial customers and over 4300 startups use Neo4j. Flagship customers...
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Market metrics

Neo4j boasts the world's largest graph database ecosystem with more than 140 million...
» more

Licensing and pricing models

GPL v3 license that can be used all the places where you might use MySQL. Neo4j Commercial...
» more

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