DBMS > Amazon Neptune vs. GeoSpock vs. Microsoft Azure Cosmos DB vs. Microsoft Azure SQL Database vs. Microsoft Azure Table Storage

System Properties Comparison Amazon Neptune vs. GeoSpock vs. Microsoft Azure Cosmos DB vs. Microsoft Azure SQL Database vs. Microsoft Azure Table Storage

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Fast, reliable graph database built for the cloud

Spatial and temporal data processing engine for extreme data scale

Globally distributed, horizontally scalable, multi-model database service

Database as a Service offering with high compatibility to Microsoft SQL Server

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Graph DBMS
RDF store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Wide column store

Secondary database models

Time Series DBMS

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Website

aws.amazon.com/neptune

geospock.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/products/azure-sql/database

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

aws.amazon.com/neptune/developer-resources

learn.microsoft.com/azure/cosmos-db

docs.microsoft.com/en-us/azure/azure-sql

Developer

Amazon

GeoSpock

Microsoft

Initial release

2017

2014

2010

2012

Current release

2.0, September 2019

V12

License

Commercial or Open Source

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, Javascript

C++

Server operating systems

hosted

Data scheme

schema-free

yes

schema-free

yes

schema-free

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

temporal, categorical

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

SQL-like query language

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC

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

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

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

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

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

Server-side scripts

Stored procedures

JavaScript

Transact SQL

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic sharding

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

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, with always 3 replicas available

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

optimistic locking

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)

Access rights for users can be defined per table

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

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