DBMS > Adabas vs. Amazon Redshift vs. Google Cloud Bigtable vs. ITTIA vs. Riak TS

System Properties Comparison Adabas vs. Amazon Redshift vs. Google Cloud Bigtable vs. ITTIA vs. Riak TS

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Edge Time Series DBMS with Real-Time Stream Processing

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Multivalue DBMS

Relational DBMS

Key-value store
Wide column store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	2.79
Rank	#102	Overall
	#2	Multivalue DBMS

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.44
Rank	#267	Overall
	#23	Time Series DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Website

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

aws.amazon.com/redshift

cloud.google.com/bigtable

www.ittia.com

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/bigtable/docs

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Software AG

Amazon (based on PostgreSQL)

Google

ITTIA L.L.C.

Open Source, formerly Basho Technologies

Initial release

1971

2012

2015

2007

2015

Current release

8.7

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

Erlang

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

hosted

Linux
Windows

Linux
OS X

Data scheme

yes

schema-free

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

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

restricted

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

does not fully support an SQL-standard

yes

yes, limited

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

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
RESTful HTTP API
WebSocket

HTTP API
Native Erlang Interface

Supported programming languages

Natural

All languages supporting JDBC/ODBC

C#
C++
Go
Java
JavaScript (Node.js)
Python

C
C++
JavaScript (Node.js)
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

in Natural

user defined functions

in Python

yes

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

links between datasets can be stored

Transaction concepts

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

ACID

Atomic single-row operations

yes

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

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Database file passwords

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