DBMS > Amazon Neptune vs. Google BigQuery vs. SAP HANA vs. Stardog vs. Tibero

System Properties Comparison Amazon Neptune vs. Google BigQuery vs. SAP HANA vs. Stardog vs. Tibero

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Large scale data warehouse service with append-only tables

In-memory, column based data store. Available as appliance or cloud service

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Website

aws.amazon.com/neptune

cloud.google.com/bigquery

www.sap.com/products/hana.html

www.stardog.com

us.tmaxsoft.com/products/tibero

Technical documentation

aws.amazon.com/neptune/developer-resources

cloud.google.com/bigquery/docs

help.sap.com/hana

docs.stardog.com

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Amazon

Google

SAP

Stardog-Union

TmaxSoft

Initial release

2017

2010

2003

Current release

2.0 SPS07 (April 4, 2023), April 2023

7.3.0, May 2020

6, April 2015

License

Commercial or Open Source

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and Assembler

Server operating systems

hosted

Appliance or cloud-service

Linux
macOS
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

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.

Import/export of XML data possible

yes

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

RESTful HTTP/JSON API

JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in JavaScript

SQLScript, R

user defined functions and aggregates, HTTP Server extensions in Java

Persistent Stored Procedure (PSM)

Triggers

yes

via event handlers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

none

horizontal partitioning

by range, hash, list or composite

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

Multi-source replication in HA-Cluster

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

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

relationships in graphs

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying 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

planned for next version

User concepts

Access control

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

Access rights for users and roles

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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