DBMS > BigchainDB vs. Microsoft Access vs. Neo4j vs. SiriDB vs. Snowflake

System Properties Comparison BigchainDB vs. Microsoft Access vs. Neo4j vs. SiriDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

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

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings

Open Source Time Series DBMS

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

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

Score	0.76
Rank	#216	Overall
	#36	Document stores

Score	93.76
Rank	#12	Overall
	#8	Relational DBMS

Score	42.68
Rank	#21	Overall
	#1	Graph DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

www.bigchaindb.com

www.microsoft.com/en-us/microsoft-365/access

neo4j.com

siridb.com

www.snowflake.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

developer.microsoft.com/en-us/access

neo4j.com/docs

docs.siridb.com

docs.snowflake.net/manuals/index.html

Developer

Microsoft

Neo4j, Inc.

Cesbit

Snowflake Computing Inc.

Initial release

2016

1992

2007

2017

2014

Current release

1902 (16.0.11328.20222), March 2019

5.23, August 2024

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Bundled with Microsoft Office

Open Source

GPL version3, commercial licenses available

Open Source

MIT License

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.

Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.

Implementation language

Python

C++

Java, Scala

Server operating systems

Linux

Windows

Not a real database server, but making use of DLLs

Linux

Can also be used server-less as embedded Java database.
OS X
Solaris
Windows

Linux

hosted

Data scheme

schema-free

yes

schema-free and schema-optional

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

Numeric data

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

Secondary indexes

yes

pluggable indexing subsystem, by default Apache Lucene

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

yes

APIs and other access methods

CLI Client
RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

yes

User defined Procedures and Functions

user defined functions

Triggers

yes

since Access 2010 using the ACE-engine

yes

via event handler

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes using Neo4j Fabric

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Causal Clustering using Raft protocol

available in in Enterprise Version only

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

but no files for transaction logging

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

a simple user-level security was built in till version Access 2003

Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

simple rights management via user accounts

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

GPL v3 license that can be used all the places where you might use MySQL. Neo4j Commercial...
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