DBMS > AllegroGraph vs. Couchbase vs. Kinetica vs. Microsoft Azure Synapse Analytics

System Properties Comparison AllegroGraph vs. Couchbase vs. Kinetica vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

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

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Fully vectorized database across both GPUs and CPUs

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.84
Rank	#204	Overall
	#35	Document stores
	#18	Graph DBMS
	#7	RDF stores
	#10	Vector DBMS

Score	16.74
Rank	#33	Overall
	#5	Document stores

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Website

allegrograph.com

www.couchbase.com

www.kinetica.com

azure.microsoft.com/services/synapse-analytics

Technical documentation

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

docs.couchbase.com

docs.kinetica.com

docs.microsoft.com/azure/synapse-analytics

Developer

Franz Inc.

Couchbase, Inc.

Kinetica

Microsoft

Initial release

2004

2011

2012

2016

Current release

8.0, December 2023

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

7.1, August 2021

License

Commercial or Open Source

commercial

Limited community edition free

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

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, C++, Go and Erlang

C, C++

C++

Server operating systems

Linux
OS X
Windows

Linux

hosted

Data scheme

yes

RDF schemas

schema-free

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.

bulk load of XML files possible

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP API
SPARQL

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

C#
Java
PHP

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

user defined functions

Transact SQL

Triggers

yes

via the TAP protocol

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

with Federation

Automatic Sharding

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

including cross data center replication
Source-replica replication

Source-replica replication

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

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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

In-memory capabilities

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

yes

Ephemeral buckets

yes

GPU vRAM or System RAM

User concepts

Access control

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

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Access rights for users and roles on table level

yes

More information provided by the system vendor

News

AllegroGraph Named a 2024 Trend Setting Product
16 September 2024

Gartner recognized Franz Inc. as a Key Neuro-Symbolic AI Provider in 2024 Hype Cycle for AI
10 September 2024

Next-Generation Cybersecurity: Integrating Knowledge Graphs and Neuro-Symbolic AI with STIX and TAXII
10 September 2024

Pruning Cycles in UMLS Metathesaurus: A Neuro Symbolic AI Approach
9 September 2024

Podcast – Gartner AI Hype Cycle – Knowledge Graphs and Neuro-Symbolic AI
24 August 2024

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