DBMS > AnzoGraph DB vs. atoti vs. Kdb

System Properties Comparison AnzoGraph DB vs. atoti vs. Kdb

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Editorial information provided by DB-Engines

Name

Description

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

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

High performance Time Series DBMS

Primary database model

Graph DBMS
RDF store

Object oriented DBMS

Time Series DBMS
Vector DBMS

Secondary database models

Relational DBMS

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

Score	0.21
Rank	#308	Overall
	#26	Graph DBMS
	#15	RDF stores

Score	0.49
Rank	#244	Overall
	#11	Object oriented DBMS

Score	7.10
Rank	#48	Overall
	#2	Time Series DBMS
	#4	Vector DBMS

Website

cambridgesemantics.com/anzograph

atoti.io

kx.com

Technical documentation

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

docs.atoti.io

code.kx.com

Developer

Cambridge Semantics

ActiveViam

Kx Systems, a division of First Derivatives plc

Initial release

2018

2000

kdb was released 2000, kdb+ in 2003

Current release

2.3, January 2021

3.6, May 2018

License

Commercial or Open Source

commercial

free trial version available

commercial

free versions available

commercial

free 32-bit version

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Data scheme

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.

yes

Secondary indexes

yes

table attribute 'grouped'

SQL

Support of SQL

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

Multidimensional Expressions (MDX)

SQL-like query language (q)

APIs and other access methods

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

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

Supported programming languages

C++
Java
Python

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

Python

user defined functions

Triggers

yes

with views

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding, horizontal partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

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

yes, multi-version concurrency control (MVCC)

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

Access rights for users and roles

rights management via user accounts

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More resources

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