DBMS > Faircom DB vs. Microsoft Azure Synapse Analytics vs. ObjectBox vs. Stardog vs. Tarantool

System Properties Comparison Faircom DB vs. Microsoft Azure Synapse Analytics vs. ObjectBox vs. Stardog vs. Tarantool

Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Lightweight, fast on-device database for IoT, Mobile and Embedded devices, persisting and synchronising objects and vectors

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
Relational DBMS

Relational DBMS

Object oriented DBMS
Vector DBMS

Graph DBMS
RDF store

Document store
Key-value store
Relational DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

with Tarantool/GIS extension

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

Score	0.18
Rank	#314	Overall
	#45	Key-value stores
	#141	Relational DBMS

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Score	1.08
Rank	#179	Overall
	#6	Object oriented DBMS
	#9	Vector DBMS

Score	1.93
Rank	#121	Overall
	#10	Graph DBMS
	#6	RDF stores

Score	1.63
Rank	#139	Overall
	#25	Document stores
	#24	Key-value stores
	#65	Relational DBMS

Website

www.faircom.com/products/faircom-db

azure.microsoft.com/services/synapse-analytics

github.com/objectbox
objectbox.io

www.stardog.com

www.tarantool.io

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

docs.microsoft.com/azure/synapse-analytics

docs.objectbox.io

docs.stardog.com

www.tarantool.io/en/doc

Developer

FairCom Corporation

Microsoft

ObjectBox Limited

Stardog-Union

Initial release

1979

2016

2017

2010

2008

Current release

V13, July 2024

4.0 (May 2024)

7.3.0, May 2020

2.10.0, May 2022

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Bindings are released under Apache 2.0

Apache License 2.0

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

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

ANSI C, C++

C++

C and C++

Java

C and C++

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

Linux
macOS
Windows

BSD
Linux
macOS

Data scheme

schema free, schema optional, schema required, partial schema,

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, ANSI SQL Types, JSON, typed binary structures

yes

yes, plus "flex" map-like types

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

yes, ANSI SQL with proprietary extensions

yes

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

Full-featured ANSI SQL support

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

ADO.NET
JDBC
ODBC

Proprietary native API

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

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C#
Java
PHP

C
C++
Dart (Flutter)
Go
Java
Kotlin
Python
Swift

.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

.Net, JavaScript, C/C++

Transact SQL

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

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding, horizontal partitioning

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

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

Data sync between devices allowing occasional connected databases to work completely offline

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate 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

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

relationships in graphs

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

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, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

yes

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

More information provided by the system vendor

News

MongoDB Realm & Device Sync alternatives – ObjectBox
17 September 2024

Local AI – what it is and why we need it
11 September 2024

First on-device Vector Database (aka Semantic Index) for iOS
24 July 2024

Retrieval Augmented Generation (RAG) with vector databases: Expanding AI Capabilities
18 June 2024

The on-device Vector Database for Android and Java
29 May 2024

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Related products and services

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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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