DBMS > Amazon Redshift vs. EJDB vs. IBM Db2 Event Store vs. Oracle NoSQL

System Properties Comparison Amazon Redshift vs. EJDB vs. IBM Db2 Event Store vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Distributed Event Store optimized for Internet of Things use cases

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Relational DBMS

Document store

Event Store
Time Series DBMS

Document store
Key-value store
Relational DBMS

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	0.30
Rank	#294	Overall
	#44	Document stores

Score	0.23
Rank	#316	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	2.96
Rank	#103	Overall
	#18	Document stores
	#17	Key-value stores
	#52	Relational DBMS

Website

aws.amazon.com/redshift

github.com/Softmotions/ejdb

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

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

docs.aws.amazon.com/redshift

github.com/Softmotions/ejdb/blob/master/README.md

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

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Amazon (based on PostgreSQL)

Softmotions

IBM

Oracle

Initial release

2012

2017

2011

Current release

2.0

23.3, December 2023

License

Commercial or Open Source

commercial

Open Source

GPLv2

commercial

free developer edition available

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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++

Java

Server operating systems

hosted

server-less

Linux

Linux, macOS, Windows for the developer addition

Linux
Solaris SPARC/x86

Data scheme

yes

schema-free

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

optional

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

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

in-process shared library

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

C
C#
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Active-active shard replication

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

Read/Write 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

off heap cache

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

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