DBMS > etcd vs. IBM Cloudant vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. RavenDB

System Properties Comparison etcd vs. IBM Cloudant vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. RavenDB

Editorial information provided by DB-Engines

Name

Description

A distributed reliable key-value store

Database as a Service offering based on Apache CouchDB

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Key-value store

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Document store

Secondary database models

Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

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

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	2.92
Rank	#101	Overall
	#18	Document stores

Website

etcd.io
github.com/etcd-io/etcd

www.ibm.com/products/cloudant

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

ravendb.net

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

ravendb.net/docs

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

Hibernating Rhinos

Initial release

2010

2014

2012

2010

Current release

3.4, August 2019

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

AGPL version 3, 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

Erlang

Server operating systems

FreeBSD
Linux
Windows

experimental

hosted

Linux
macOS
Raspberry Pi
Windows

Data scheme

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (RQL)

APIs and other access methods

gRPC
JSON over HTTP

RESTful HTTP/JSON API

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

RESTful HTTP API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

JavaScript

yes

Triggers

yes, watching key changes

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

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

optimistic locking

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

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.

User concepts

Access control

Access rights for users can be defined per database

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

Authorization levels configured per client per database

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.