DBMS > Atos Standard Common Repository vs. BigchainDB vs. BigObject vs. Google Cloud Spanner vs. Snowflake

System Properties Comparison Atos Standard Common Repository vs. BigchainDB vs. BigObject vs. Google Cloud Spanner 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

Analytic DBMS for real-time computations and queries

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.

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

Primary database model

Document store
Key-value store

Document store

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

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

www.bigchaindb.com

bigobject.io

cloud.google.com/spanner

www.snowflake.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.bigobject.io

cloud.google.com/spanner/docs

docs.snowflake.net/manuals/index.html

Developer

Atos Convergence Creators

BigObject, Inc.

Google

Snowflake Computing Inc.

Initial release

2016

2015

2017

2014

Current release

1703

License

Commercial or Open Source

commercial

Open Source

AGPL v3

commercial

free community edition available

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

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

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

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

SQL-like DML and DDL statements

yes

Query statements complying to ANSI 2011

yes

APIs and other access methods

LDAP

CLI Client
RESTful HTTP API

fluentd
ODBC
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

CLI Client
JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

Go
Haskell
Java
JavaScript
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

none

Multi-source replication with 3 replicas for regional instances.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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)

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

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