DBMS > Atos Standard Common Repository vs. BigchainDB vs. JanusGraph vs. Snowflake vs. Splice Machine

System Properties Comparison Atos Standard Common Repository vs. BigchainDB vs. JanusGraph vs. Snowflake vs. Splice Machine

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store
Key-value store

Document store

Graph DBMS

Relational DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.janusgraph.org

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

Developer

Atos Convergence Creators

Linux Foundation; originally developed as Titan by Aurelius

Snowflake Computing Inc.

Splice Machine

Initial release

2016

2017

2014

Current release

1703

0.6.3, February 2023

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

AGPL v3

Open Source

Apache 2.0

commercial

Open Source

AGPL 3.0, commercial license available

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

Java

Python

Java

Server operating systems

Linux

Linux
OS X
Unix
Windows

hosted

Linux
OS X
Solaris
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

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

yes

Typing

predefined data types such as float or date

optional

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

SQL

Support of SQL

yes

APIs and other access methods

LDAP

CLI Client
RESTful HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages with LDAP bindings

Go
Haskell
Java
JavaScript
Python
Ruby

Clojure
Java
Python

JavaScript (Node.js)
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions

yes

Java

Triggers

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

LDAP bind authentication

yes

User authentification and security via Rexster Graph Server

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

Access rights for users, groups and roles according to SQL-standard

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