DBMS > AnzoGraph DB vs. InfinityDB vs. Snowflake vs. SurrealDB vs. Teradata

System Properties Comparison AnzoGraph DB vs. InfinityDB vs. Snowflake vs. SurrealDB vs. Teradata

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 Java embedded Key-Value Store which extends the Java Map interface

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

A fully ACID transactional, developer-friendly, multi-model DBMS

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Graph DBMS
RDF store

Key-value store

Relational DBMS

Document store
Graph DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series 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	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

cambridgesemantics.com/anzograph

Technical documentation

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

boilerbay.com/infinitydb/manual

docs.snowflake.net/manuals/index.html

surrealdb.com/docs

docs.teradata.com

Developer

Cambridge Semantics

Boiler Bay Inc.

Snowflake Computing Inc.

SurrealDB Ltd

Teradata

Initial release

2018

2002

2014

2022

1984

Current release

2.3, January 2021

4.0

v1.1.1, January 2024

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Rust

Server operating systems

Linux

All OS with a Java VM

hosted

Linux
macOS
Windows

hosted
Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

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

schema-free

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

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

SQL

Support of SQL

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

yes

SQL-like query language

yes

SQL 2016 + extensions

APIs and other access methods

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

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

CLI Client
JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

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

Supported programming languages

C++
Java
Python

Java

JavaScript (Node.js)
Python

Deno
Go
JavaScript (Node.js)
Rust

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

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

yes

UDFs, stored procedures, table functions in parallel

Triggers

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

yes

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

none

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

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

Access rights for users and roles

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

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

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