DBMS > AllegroGraph vs. EsgynDB vs. Google Cloud Bigtable vs. Hyprcubd vs. SAP HANA

System Properties Comparison AllegroGraph vs. EsgynDB vs. Google Cloud Bigtable vs. Hyprcubd vs. SAP HANA

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

High performance, persistent RDF store with additional support for Graph DBMS

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Serverless Time Series DBMS

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

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Relational DBMS

Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.13
Rank	#179	Overall
	#30	Document stores
	#17	Graph DBMS
	#7	RDF stores
	#7	Vector DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

allegrograph.com

www.esgyn.cn

cloud.google.com/bigtable

hyprcubd.com (offline)

www.sap.com/products/hana.html

Technical documentation

franz.com/agraph/support/documentation/current/agraph-introduction.html

cloud.google.com/bigtable/docs

help.sap.com/hana

Developer

Franz Inc.

Esgyn

Google

Hyprcubd, Inc.

SAP

Initial release

2004

2015

2010

Current release

8.0, December 2023

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Limited community edition free

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

C++, Java

Server operating systems

Linux
OS X
Windows

Linux

hosted

Appliance or cloud-service

Data scheme

yes

RDF schemas

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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.

bulk load of XML files possible

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

yes

SQL-like query language

yes

APIs and other access methods

RESTful HTTP API
SPARQL

ADO.NET
JDBC
ODBC

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

gRPC (https)

JDBC
ODBC

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

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

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

Java Stored Procedures

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

with Federation

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication between multi datacenters

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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 capabilities

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

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

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

token access

yes

More information provided by the system vendor

Specific characteristics

Knowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more

Competitive advantages

AllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more

News

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9 February 2024

What is Neuro-Symbolic AI?
23 January 2024

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