DBMS > Badger vs. IBM Cloudant vs. IBM Db2 Event Store vs. Kinetica vs. Microsoft Azure Cosmos DB

System Properties Comparison Badger vs. IBM Cloudant vs. IBM Db2 Event Store vs. Kinetica vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Database as a Service offering based on Apache CouchDB

Distributed Event Store optimized for Internet of Things use cases

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Key-value store

Document store

Event Store
Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

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

Website

github.com/dgraph-io/badger

www.ibm.com/products/cloudant

www.ibm.com/products/db2-event-store

www.kinetica.com

azure.microsoft.com/services/cosmos-db

Technical documentation

godoc.org/github.com/dgraph-io/badger

cloud.ibm.com/docs/Cloudant

www.ibm.com/docs/en/db2-event-store

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

Developer

DGraph Labs

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

IBM

Kinetica

Microsoft

Initial release

2017

2010

2017

2012

2014

Current release

2.0

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free developer 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

C and C++

C, C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux

hosted

Data scheme

schema-free

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

yes

through the embedded Spark runtime

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C++
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

yes

user defined functions

JavaScript

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Active-active shard 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

none

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

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

Optimistic locking

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

GPU vRAM or System RAM

User concepts

Access control

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

Access rights can be defined down to the item level

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