DBMS > AnzoGraph DB vs. Infobright vs. JaguarDB vs. Spark SQL

System Properties Comparison AnzoGraph DB vs. Infobright vs. JaguarDB vs. Spark SQL

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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

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Performant, highly scalable DBMS for AI and IoT applications

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store
Vector DBMS

Relational 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	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

cambridgesemantics.com/anzograph

ignitetech.com/softwarelibrary/infobrightdb

www.jaguardb.com

spark.apache.org/sql

Technical documentation

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

www.jaguardb.com/support.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Cambridge Semantics

Ignite Technologies Inc.; formerly InfoBright Inc.

DataJaguar, Inc.

Apache Software Foundation

Initial release

2018

2005

2015

2014

Current release

2.3, January 2021

3.3 July 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free trial version available

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

GPL V3.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

the server part. Clients available in other languages

Scala

Server operating systems

Linux

Linux
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.

Secondary indexes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

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

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like DML and DDL statements

APIs and other access methods

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

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

C++
Java
Python

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

Multi-source replication

none

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

Foreign keys

Referential integrity

not needed in graphs

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

exploiting MySQL or PostgreSQL frontend capabilities

rights management via user accounts

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More resources

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