DBMS > Atos Standard Common Repository vs. BigchainDB vs. Google Cloud Spanner vs. Microsoft Azure Cosmos DB vs. Snowflake

System Properties Comparison Atos Standard Common Repository vs. BigchainDB vs. Google Cloud Spanner vs. Microsoft Azure Cosmos DB vs. Snowflake

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 horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Document store
Key-value store

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.76
Rank	#213	Overall
	#35	Document stores

Score	2.70
Rank	#100	Overall
	#51	Relational DBMS

Score	23.95
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	142.50
Rank	#7	Overall
	#5	Relational DBMS

Website

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

www.bigchaindb.com

cloud.google.com/spanner

azure.microsoft.com/services/cosmos-db

www.snowflake.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

cloud.google.com/spanner/docs

learn.microsoft.com/azure/cosmos-db

docs.snowflake.net/manuals/index.html

Developer

Atos Convergence Creators

Google

Microsoft

Snowflake Computing Inc.

Initial release

2016

2017

2014

Current release

1703

License

Commercial or Open Source

commercial

Open Source

AGPL v3

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

Java

Python

Server operating systems

Linux

hosted

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

schema-free

yes

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

Typing

predefined data types such as float or date

optional

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.

yes

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

SQL-like query language

yes

APIs and other access methods

LDAP

CLI Client
RESTful HTTP API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

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

CLI Client
JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

Go
Haskell
Java
JavaScript
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

JavaScript

user defined functions

Triggers

yes

JavaScript

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication with 3 replicas for regional instances.

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Strict serializable isolation

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

LDAP bind authentication

yes

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights can be defined down to the item level

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

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