DBMS > JaguarDB vs. Netezza vs. Stardog vs. Tarantool

System Properties Comparison JaguarDB vs. Netezza vs. Stardog vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

Performant, highly scalable DBMS for AI and IoT applications

Data warehouse and analytics appliance part of IBM PureSystems

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Key-value store
Vector DBMS

Relational DBMS

Graph DBMS
RDF store

Document store
Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

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

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Website

www.jaguardb.com

www.ibm.com/products/netezza

www.stardog.com

www.tarantool.io

Technical documentation

www.jaguardb.com/support.html

docs.stardog.com

www.tarantool.io/en/doc

Developer

DataJaguar, Inc.

IBM

Stardog-Union

Initial release

2015

2000

2010

2008

Current release

3.3 July 2023

7.3.0, May 2020

2.10.0, May 2022

License

Commercial or Open Source

Open Source

GPL V3.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

the server part. Clients available in other languages

Java

C and C++

Server operating systems

Linux

included in appliance

Linux
macOS
Windows

BSD
Linux
macOS

Data scheme

yes

schema-free and OWL/RDFS-schema support

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

Full-featured ANSI SQL support

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
OLE DB

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Open binary protocol

Supported programming languages

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

C
C++
Fortran
Java
Lua
Perl
Python
R

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

yes

user defined functions and aggregates, HTTP Server extensions in Java

Lua, C and SQL stored procedures

Triggers

yes

via event handlers

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

Multi-source replication in HA-Cluster

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency in HA-Cluster

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

yes

relationships in graphs

yes

Transaction concepts

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

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, write ahead logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

rights management via user accounts

Users with fine-grained authorization concept

Access rights for users and roles

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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Recent citations in the news

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11 July 2023, ibm.com

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Migrating your Netezza data warehouse to Amazon Redshift | Amazon Web Services
27 May 2020, AWS Blog

IBM Brings Back a Netezza, Attacks Yellowbrick
29 June 2020, Datanami

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12 August 2020, ibm.com

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