DBMS > Cassandra vs. EXASOL vs. IBM Db2 vs. IBM Db2 Event Store

System Properties Comparison Cassandra vs. EXASOL vs. IBM Db2 vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Common in IBM host environments, 2 different versions for host and Windows/Linux

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Wide column store

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Event Store
Time Series DBMS

Secondary database models

Vector DBMS

starting with release V5

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

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

Website

cassandra.apache.org

www.exasol.com

www.ibm.com/products/db2

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

Technical documentation

cassandra.apache.org/doc/latest

www.exasol.com/resources

www.ibm.com/docs/en/db2

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

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

Exasol

IBM

Initial release

2008

2000

1983

host version

2017

Current release

4.1.3, July 2023

12.1, October 2016

2.0

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free version is available

commercial

free developer edition available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

C and C++

Server operating systems

BSD
Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux

Linux, macOS, Windows for the developer addition

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

SQL-like SELECT, DML and DDL statements (CQL)

yes

through the embedded Spark runtime

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

.Net
JDBC
ODBC
WebSocket

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

Java
Lua
Python
R

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

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

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

only with Windows/Unix/Linux Version

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

yes

with separate tools (MQ, InfoSphere)

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

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

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 can be defined per object

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
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Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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