DBMS > AnzoGraph DB vs. Microsoft SQL Server vs. SpatiaLite vs. Teradata Aster vs. YDB

System Properties Comparison AnzoGraph DB vs. Microsoft SQL Server vs. SpatiaLite vs. Teradata Aster vs. YDB

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

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

Spatial extension of SQLite

Platform for big data analytics on multistructured data sources and types

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Document store
Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	1.63
Rank	#146	Overall
	#3	Spatial DBMS

Score	0.33
Rank	#287	Overall
	#43	Document stores
	#132	Relational DBMS

Website

cambridgesemantics.com/anzograph

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

www.gaia-gis.it/fossil/libspatialite/index

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

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

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

www.gaia-gis.it/gaia-sins/spatialite_topics.html

ydb.tech/en/docs

Developer

Cambridge Semantics

Microsoft

Alessandro Furieri

Teradata

Yandex

Initial release

2018

1989

2008

2005

2019

Current release

2.3, January 2021

SQL Server 2022, November 2022

5.0.0, August 2020

License

Commercial or Open Source

commercial

free trial version available

commercial

restricted free version is available

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

commercial

Open Source

Apache 2.0; commercial license available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Server operating systems

Linux

Linux
Windows

server-less

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Flexible Schema (defined schema, partial schema, schema free)

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

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

yes

SQL-like query language (YQL)

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)

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

C++
Java
Python

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

C
C#
C++
Java
Python
R

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

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

R packages

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes, but depending on the SQL-Server Edition

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

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 defined for Yandex Cloud users

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