DBMS > Amazon Redshift vs. BigObject vs. OpenEdge vs. TerminusDB vs. Vitess

System Properties Comparison Amazon Redshift vs. BigObject vs. OpenEdge vs. TerminusDB vs. Vitess

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for real-time computations and queries

Application development environment with integrated database management system

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store
RDF store

Document store
Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

aws.amazon.com/redshift

bigobject.io

www.progress.com/openedge

terminusdb.com

vitess.io

Technical documentation

docs.aws.amazon.com/redshift

docs.bigobject.io

documentation.progress.com/output/ua/OpenEdge_latest

terminusdb.github.io/terminusdb/#

vitess.io/docs

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Progress Software Corporation

DataChemist Ltd.

The Linux Foundation, PlanetScale

Initial release

2012

2015

1984

2018

2013

Current release

OpenEdge 12.2, March 2020

11.0.0, January 2023

15.0.2, December 2022

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

GPL V3

Open Source

Apache Version 2.0, commercial licenses available

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

Prolog, Rust

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

AIX
HP-UX
Linux
Solaris
Windows

Linux

Docker
Linux
macOS

Data scheme

yes

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

close to SQL 92

SQL-like query language (WOQL)

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Progress proprietary ABL (Advanced Business Language)

JavaScript
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

since Version 11.4

Graph Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication

Journaling Streams

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

in-memory journaling

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

fine grained access rights according to SQL-standard

Users and groups

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

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