DBMS > AnzoGraph DB vs. Microsoft SQL Server vs. Splice Machine vs. TigerGraph vs. WakandaDB

System Properties Comparison AnzoGraph DB vs. Microsoft SQL Server vs. Splice Machine vs. TigerGraph vs. WakandaDB

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

Microsofts flagship relational DBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Secondary database models

Document store
Graph DBMS
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	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

cambridgesemantics.com/anzograph

www.microsoft.com/en-us/sql-server

splicemachine.com

www.tigergraph.com

wakanda.github.io

Technical documentation

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

learn.microsoft.com/en-US/sql/sql-server

splicemachine.com/how-it-works

docs.tigergraph.com

wakanda.github.io/doc

Developer

Cambridge Semantics

Microsoft

Splice Machine

Wakanda SAS

Initial release

2018

1989

2014

2017

2012

Current release

2.3, January 2021

SQL Server 2022, November 2022

3.1, March 2021

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

free trial version available

commercial

restricted free version is available

Open Source

AGPL 3.0, commercial license available

commercial

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

C++, JavaScript

Server operating systems

Linux

Linux
Windows

Linux
OS X
Solaris
Windows

Linux

Linux
OS X
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

SQL

Support of SQL

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

yes

SQL-like query language (GSQL)

APIs and other access methods

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
Native Spark Datasource
ODBC

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

C++
Java
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C++
Java

JavaScript

Server-side scripts

Stored procedures

user defined functions and aggregates

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Yes, via Full Spark Integration

yes

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

Relationships in graphs

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

Role-based access control

yes

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