DBMS > Amazon Neptune vs. AnzoGraph DB vs. JaguarDB vs. SAP IQ vs. Snowflake

System Properties Comparison Amazon Neptune vs. AnzoGraph DB vs. JaguarDB vs. SAP IQ vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Performant, highly scalable DBMS for AI and IoT applications

Columnar RDBMS optimized for Big Data analytics

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Graph DBMS
RDF store

Key-value store
Vector DBMS

Relational DBMS

column-oriented

Relational 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	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

aws.amazon.com/neptune

cambridgesemantics.com/anzograph

www.jaguardb.com

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

www.snowflake.com

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

www.jaguardb.com/support.html

help.sap.com/docs/SAP_IQ

docs.snowflake.net/manuals/index.html

Developer

Amazon

Cambridge Semantics

DataJaguar, Inc.

SAP, formerly Sybase

Snowflake Computing Inc.

Initial release

2017

2018

2015

1994

2014

Current release

2.3, January 2021

3.3 July 2023

16.1 SPS04, April 2019

License

Commercial or Open Source

commercial

free trial version available

Open Source

GPL V3.0

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++

the server part. Clients available in other languages

Server operating systems

hosted

Linux

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

schema-free

Schema-free and OWL/RDFS-schema support

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

JDBC
ODBC

ADO.NET
JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C++
Java
Python

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

C
C#
C++
Java
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Automatic sharding

Sharding

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes

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.

Multi-source replication in MPP-Cluster

Multi-source replication

SAP/Sybase Replication Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

not needed in graphs

yes

Transaction concepts

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

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.

yes

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 and roles

rights management via user accounts

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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