DBMS > AnzoGraph DB vs. MaxDB vs. Snowflake vs. Teradata vs. Vitess

System Properties Comparison AnzoGraph DB vs. MaxDB vs. Snowflake vs. Teradata vs. Vitess

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

A robust and reliable RDBMS optimized to run all major SAP solutions

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

A hybrid cloud data analytics software platform (Teradata Vantage)

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

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

Score	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

cambridgesemantics.com/anzograph

Technical documentation

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

maxdb.sap.com/documentation

docs.snowflake.net/manuals/index.html

docs.teradata.com

vitess.io/docs

Developer

Cambridge Semantics

SAP, acquired from Software AG (Adabas-D) in 1997

Snowflake Computing Inc.

Teradata

The Linux Foundation, PlanetScale

Initial release

2018

1984

2014

1984

2013

Current release

2.3, January 2021

7.9.10.12, February 2024

Teradata Vantage 1.0 MU2, January 2019

15.0.2, December 2022

License

Commercial or Open Source

commercial

free trial version available

commercial

Limited community edition free

commercial

Open Source

Apache Version 2.0, commercial licenses available

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++

Server operating systems

Linux

AIX
HP-UX
Linux
Solaris
Windows

hosted

hosted
Linux

Docker
Linux
macOS

Data scheme

Schema-free and OWL/RDFS-schema support

yes

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

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

Join-index to prejoin tables, aggregate index, sparse index, hash index

yes

SQL

Support of SQL

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

yes

SQL 2016 + extensions

yes

with proprietary extensions

APIs and other access methods

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

ADO.NET
JDBC
ODBC
WebDAV

CLI Client
JDBC
ODBC

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

C++
Java
Python

.Net
C#
Java
Perl
PHP
Python

JavaScript (Node.js)
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions

yes

UDFs, stored procedures, table functions in parallel

yes

proprietary syntax

Triggers

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

yes

Sharding

Hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

not needed in graphs

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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