DBMS > Altibase vs. Amazon Redshift vs. H2GIS vs. Spark SQL

System Properties Comparison Altibase vs. Amazon Redshift vs. H2GIS vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Large scale data warehouse service for use with business intelligence tools

Spatial extension of H2

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.02
Rank	#189	Overall
	#88	Relational DBMS

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

altibase.com

aws.amazon.com/redshift

www.h2gis.org

spark.apache.org/sql

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

docs.aws.amazon.com/redshift

www.h2gis.org/docs/home

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Altibase

Amazon (based on PostgreSQL)

CNRS

Apache Software Foundation

Initial release

1999

2012

2013

2014

Current release

v7.3, 2023, August 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

Open Source

LGPL 3.0

Open Source

Apache 2.0

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++

Java

Scala

Server operating systems

AIX
HP-UX
Linux

hosted

Linux
OS X
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.

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

ANSI SQL-92

yes

does not fully support an SQL-standard

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

Supported programming languages

C
C++
Java

All languages supporting JDBC/ODBC

Java

Java
Python
R
Scala

Server-side scripts

Stored procedures

stored procedures and stored functions

user defined functions

in Python

yes

based on H2

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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)

yes

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes

based on H2

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