DBMS > Adabas vs. Amazon Redshift vs. GigaSpaces vs. Microsoft Azure Synapse Analytics vs. Spark SQL

System Properties Comparison Adabas vs. Amazon Redshift vs. GigaSpaces vs. Microsoft Azure Synapse Analytics vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

OLTP - DBMS for mainframes and Linux/Unix/Windows environments

used typically together with the Natural programming platform

Large scale data warehouse service for use with business intelligence tools

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

Primary database model

Multivalue DBMS

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.softwareag.com/en_corporate/platform/adabas-natural.html

aws.amazon.com/redshift

www.gigaspaces.com

azure.microsoft.com/services/synapse-analytics

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/redshift

docs.gigaspaces.com/latest/landing.html

docs.microsoft.com/azure/synapse-analytics

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

Developer

Software AG

Amazon (based on PostgreSQL)

Gigaspaces Technologies

Microsoft

Apache Software Foundation

Initial release

1971

2012

2000

2016

2014

Current release

15.5, September 2020

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

commercial

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

Java, C++, .Net

C++

Scala

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

hosted

Linux
macOS
Solaris
Windows

hosted

Linux
OS X
Windows

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.

XML can be used for describing objects metadata

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

does not fully support an SQL-standard

SQL-99 for query and DML statements

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

with add-on software Adabas SOA Gateway
SOAP-based API

with add-on software Adabas SOA Gateway

JDBC
ODBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Natural

All languages supporting JDBC/ODBC

.Net
C++
Java
Python
Scala

C#
Java
PHP

Java
Python
R
Scala

Server-side scripts

Stored procedures

in Natural

user defined functions

in Python

yes

Transact SQL

Triggers

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

yes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas Vista

Sharding

Sharding, horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

only with OS-specific tools (e.g. IBM RACF, CA Top Secret)

fine grained access rights according to SQL-standard

Role-based access control

yes

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.