DBMS > Adabas vs. dBASE vs. Google BigQuery vs. HEAVY.AI vs. Netezza

System Properties Comparison Adabas vs. dBASE vs. Google BigQuery vs. HEAVY.AI vs. Netezza

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

dBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.

Large scale data warehouse service with append-only tables

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Data warehouse and analytics appliance part of IBM PureSystems

Primary database model

Multivalue DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	2.79
Rank	#102	Overall
	#2	Multivalue DBMS

Score	9.70
Rank	#44	Overall
	#28	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Website

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

www.dbase.com

cloud.google.com/bigquery

github.com/heavyai/heavydb
www.heavy.ai

www.ibm.com/products/netezza

Technical documentation

www.dbase.com/support/knowledgebase

cloud.google.com/bigquery/docs

docs.heavy.ai

Developer

Software AG

Asthon Tate

Google

HEAVY.AI, Inc.

IBM

Initial release

1971

1979

2010

2016

2000

Current release

dBASE 2019, 2019

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

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 CUDA

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

DOS

dBase Classic
Windows

dBase Pro

hosted

Linux

included in appliance

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

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

APIs and other access methods

HTTP API

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

with add-on software Adabas SOA Gateway

none

The IDE can access other DBMS or ODBC-sources.

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
OLE DB

Supported programming languages

Natural

dBase proprietary IDE

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

in Natural

The IDE can access stored procedures in other database systems.

user defined functions

in JavaScript

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

none

Multi-source replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

not for dBase internal data, but IDE does support transactions when accessing external DBMS

Since BigQuery is designed for querying 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)

Access rights for users and roles

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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