DBMS > BigchainDB vs. JaguarDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Valentina Server

System Properties Comparison BigchainDB vs. JaguarDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Performant, highly scalable DBMS for AI and IoT applications

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

Object-relational database and reports server

Primary database model

Document store

Key-value store
Vector DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

www.bigchaindb.com

www.jaguardb.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

www.valentina-db.net

Technical documentation

bigchaindb.readthedocs.io/en/latest

www.jaguardb.com/support.html

learn.microsoft.com/azure/cosmos-db

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

DataJaguar, Inc.

Microsoft

Paradigma Software

Initial release

2016

2015

2014

2012

1999

Current release

3.3 July 2023

5.7.5

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

GPL V3.0

commercial

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

Python

C++

the server part. Clients available in other languages

Server operating systems

Linux

hosted

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

yes

APIs and other access methods

CLI Client
RESTful HTTP API

JDBC
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API

ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

JavaScript

yes

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

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

yes

rights management via user accounts

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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