DBMS > Amazon Redshift vs. EXASOL vs. IBM Db2 Event Store vs. SwayDB vs. VoltDB

System Properties Comparison Amazon Redshift vs. EXASOL vs. IBM Db2 Event Store vs. SwayDB vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Distributed Event Store optimized for Internet of Things use cases

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Key-value store

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

aws.amazon.com/redshift

www.exasol.com

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

swaydb.simer.au

www.voltdb.com

Technical documentation

docs.aws.amazon.com/redshift

www.exasol.com/resources

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

docs.voltdb.com

Developer

Amazon (based on PostgreSQL)

Exasol

IBM

Simer Plaha

VoltDB Inc.

Initial release

2012

2000

2017

2018

2010

Current release

2.0

11.3, April 2022

License

Commercial or Open Source

commercial

free developer edition available

Open Source

GNU Affero GPL V3.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

C and C++

Scala

Java, C++

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X

for development

Data scheme

yes

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

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

through the embedded Spark runtime

yes

only a subset of SQL 99

APIs and other access methods

JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

Java
Lua
Python
R

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

Java
Kotlin
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Active-active shard replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Atomic execution of operations

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Data access is serialized by the server

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

Snapshots and command logging

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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