DBMS > Alibaba Cloud MaxCompute vs. BigObject vs. FatDB vs. IBM Cloudant vs. Microsoft Azure Cosmos DB

System Properties Comparison Alibaba Cloud MaxCompute vs. BigObject vs. FatDB vs. IBM Cloudant vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

MaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousing

Analytic DBMS for real-time computations and queries

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Database as a Service offering based on Apache CouchDB

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

a graph-processing framework is available with the graphs being stored in tables

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Key-value store

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

www.alibabacloud.com/product/maxcompute

bigobject.io

www.ibm.com/products/cloudant

azure.microsoft.com/services/cosmos-db

Technical documentation

www.alibabacloud.com/help/en/maxcompute

docs.bigobject.io

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

Developer

Alibaba

BigObject, Inc.

FatCloud

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

Initial release

2016

2015

2012

2010

2014

License

Commercial or Open Source

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

Erlang

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Windows

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

Via inetgration in SQL Server

SQL-like query language

APIs and other access methods

Fluentd
Flume
MaxCompute Console

fluentd
ODBC
RESTful HTTP API

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

RESTful HTTP/JSON API

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

Supported programming languages

Java

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in Java

Lua

yes

via applications

View functions (Map-Reduce) in JavaScript

JavaScript

Triggers

yes

via applications

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

selectable replication factor

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Eventual Consistency
Immediate Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

atomic operations within a document possible

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Optimistic locking

yes

Durability

Support for making data persistent

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

Access rights for users and roles

Can implement custom security layer via applications

Access rights for users can be defined per database

Access rights can be defined down to the item level

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