DBMS > Amazon Redshift vs. Google Cloud Bigtable vs. OrientDB vs. SAP SQL Anywhere vs. TerminusDB

System Properties Comparison Amazon Redshift vs. Google Cloud Bigtable vs. OrientDB vs. SAP SQL Anywhere vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

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

Primary database model

Relational DBMS

Key-value store
Wide column store

Document store
Graph DBMS
Key-value store

Relational DBMS

Graph DBMS

Secondary database models

Document store
RDF store

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	3.58
Rank	#92	Overall
	#14	Key-value stores
	#8	Wide column stores

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

Score	4.57
Rank	#78	Overall
	#43	Relational DBMS

Score	0.22
Rank	#323	Overall
	#28	Graph DBMS

Website

aws.amazon.com/redshift

cloud.google.com/bigtable

orientdb.org

www.sap.com/products/technology-platform/sql-anywhere.html

terminusdb.com

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/bigtable/docs

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

help.sap.com/docs/SAP_SQL_Anywhere

terminusdb.github.io/terminusdb/#

Developer

Amazon (based on PostgreSQL)

Google

OrientDB LTD; CallidusCloud; SAP

SAP

formerly Sybase

DataChemist Ltd.

Initial release

2012

2015

2010

1992

2018

Current release

3.2.29, March 2024

17, July 2015

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Open Source

GPL V3

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

Prolog, Rust

Server operating systems

hosted

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

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

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

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 query language, no joins

yes

SQL-like query language (WOQL)

APIs and other access methods

JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

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

ADO.NET
HTTP API
JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

All languages supporting JDBC/ODBC

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

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

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

JavaScript
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Java, Javascript

yes, in C/C++, Java, .Net or Perl

yes

Triggers

Hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication

Source-replica replication

Database mirroring

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

relationship in graphs

yes

Transaction concepts

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

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users and roles; record level security configurable

fine grained access rights according to SQL-standard

Role-based access control

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