DBMS > Amazon Redshift vs. GeoMesa vs. OpenEdge vs. Splice Machine

System Properties Comparison Amazon Redshift vs. GeoMesa vs. OpenEdge vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Application development environment with integrated database management system

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Spatial DBMS

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

aws.amazon.com/redshift

www.geomesa.org

www.progress.com/openedge

splicemachine.com

Technical documentation

docs.aws.amazon.com/redshift

www.geomesa.org/documentation/stable/user/index.html

documentation.progress.com/output/ua/OpenEdge_latest

splicemachine.com/how-it-works

Developer

Amazon (based on PostgreSQL)

CCRi and others

Progress Software Corporation

Splice Machine

Initial release

2012

2014

1984

2014

Current release

5.0.0, May 2024

OpenEdge 12.2, March 2020

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

commercial

Open Source

AGPL 3.0, commercial license 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

Scala

Java

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Solaris
Windows

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.

yes

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

close to SQL 92

yes

APIs and other access methods

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Progress proprietary ABL (Advanced Business Language)

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

depending on storage layer

horizontal partitioning

since Version 11.4

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on storage layer

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

Immediate 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)

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

depending on storage layer

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

depending on the DBMS used for storage

Users and groups

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

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