DBMS > Amazon Redshift vs. atoti vs. EsgynDB vs. IBM Db2 Event Store

System Properties Comparison Amazon Redshift vs. atoti vs. EsgynDB vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Relational DBMS

Object oriented DBMS

Relational DBMS

Event Store
Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

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

Website

aws.amazon.com/redshift

atoti.io

www.esgyn.cn

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

Technical documentation

docs.aws.amazon.com/redshift

docs.atoti.io

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

Developer

Amazon (based on PostgreSQL)

ActiveViam

Esgyn

IBM

Initial release

2012

2015

2017

Current release

2.0

License

Commercial or Open Source

commercial

free versions available

commercial

free developer edition 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

Java

C++, Java

C and C++

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Data scheme

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

Multidimensional Expressions (MDX)

yes

through the embedded Spark runtime

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/ADO.Net

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

in Python

Python

Java Stored Procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

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