DBMS > Dgraph vs. Google BigQuery vs. OpenEdge vs. Oracle NoSQL vs. VelocityDB

System Properties Comparison Dgraph vs. Google BigQuery vs. OpenEdge vs. Oracle NoSQL vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Large scale data warehouse service with append-only tables

Application development environment with integrated database management system

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Graph DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Graph DBMS
Object oriented DBMS

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

Score	1.48
Rank	#158	Overall
	#15	Graph DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	3.75
Rank	#88	Overall
	#47	Relational DBMS

Score	2.96
Rank	#103	Overall
	#18	Document stores
	#17	Key-value stores
	#52	Relational DBMS

Score	0.11
Rank	#347	Overall
	#35	Graph DBMS
	#15	Object oriented DBMS

Website

dgraph.io

cloud.google.com/bigquery

www.progress.com/openedge

www.oracle.com/database/nosql/technologies/nosql

velocitydb.com

Technical documentation

dgraph.io/docs

cloud.google.com/bigquery/docs

documentation.progress.com/output/ua/OpenEdge_latest

docs.oracle.com/en/database/other-databases/nosql-database/index.html

velocitydb.com/UserGuide

Developer

Dgraph Labs, Inc.

Google

Progress Software Corporation

Oracle

VelocityDB Inc

Initial release

2016

2010

1984

2011

Current release

OpenEdge 12.2, March 2020

23.3, December 2023

7.x

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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.

Implementation language

Java

Server operating systems

Linux
OS X
Windows

hosted

AIX
HP-UX
Linux
Solaris
Windows

Linux
Solaris SPARC/x86

Any that supports .NET

Data scheme

schema-free

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

yes

optional

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

SQL

Support of SQL

yes

close to SQL 92

SQL-like DML and DDL statements

APIs and other access methods

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

RESTful HTTP/JSON API

JDBC
ODBC

RESTful HTTP API

.Net

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Progress proprietary ABL (Advanced Business Language)

C
C#
Go
Java
JavaScript (Node.js)
Python

.Net

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

yes

none

horizontal partitioning

since Version 11.4

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Source-replica replication

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

configurable

ACID within a storage node (=shard)

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

off heap cache

yes

User concepts

Access control

Planned for future releases

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

Google Cloud Identity & Access Management (IAM)

Users and groups

Access rights for users and roles

Based on Windows Authentication

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