DBMS > Amazon Neptune vs. Coveo vs. Microsoft Azure Cosmos DB vs. PostGIS vs. SAP Adaptive Server

System Properties Comparison Amazon Neptune vs. Coveo vs. Microsoft Azure Cosmos DB vs. PostGIS vs. SAP Adaptive Server

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

Globally distributed, horizontally scalable, multi-model database service

Spatial extension of PostgreSQL

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Primary database model

Graph DBMS
RDF store

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Spatial DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Relational DBMS

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	2.11
Rank	#118	Overall
	#11	Search engines

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Score	35.09
Rank	#25	Overall
	#17	Relational DBMS

Website

aws.amazon.com/neptune

www.coveo.com

azure.microsoft.com/services/cosmos-db

postgis.net

www.sap.com/products/technology-platform/sybase-ase.html

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.coveo.com

learn.microsoft.com/azure/cosmos-db

postgis.net/documentation

help.sap.com/docs/SAP_ASE

Developer

Amazon

Coveo

Microsoft

SAP, Sybase

Initial release

2017

2012

2014

2005

1987

Current release

3.4.2, February 2024

16.0

License

Commercial or Open Source

commercial

Open Source

GPL v2.0

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.

Implementation language

C and C++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Unix
Windows

Data scheme

schema-free

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C#
Java
JavaScript
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

JavaScript

user defined functions

Java and Transact-SQL

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

Implicit feature of the cloud service

yes

based on PostgreSQL

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

yes

Implicit feature of the cloud service

yes

based on PostgreSQL

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

yes

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

granular access controls, API key management, content filters

Access rights can be defined down to the item level

yes

based on PostgreSQL

fine grained access rights according to SQL-standard

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