DBMS > GridDB vs. IBM Db2 vs. Sadas Engine vs. SAP HANA vs. Teradata

System Properties Comparison GridDB vs. IBM Db2 vs. Sadas Engine vs. SAP HANA vs. Teradata

Editorial information provided by DB-Engines

Name

Description

Scalable in-memory time series database optimized for IoT and Big Data

Common in IBM host environments, 2 different versions for host and Windows/Linux

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

In-memory, column based data store. Available as appliance or cloud service

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Time Series DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Secondary database models

Key-value store
Relational DBMS

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	125.90
Rank	#9	Overall
	#6	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

griddb.net

www.ibm.com/products/db2

www.sadasengine.com

www.sap.com/products/hana.html

www.teradata.com

Technical documentation

docs.griddb.net

www.ibm.com/docs/en/db2

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

help.sap.com/hana

docs.teradata.com

Developer

Toshiba Corporation

IBM

SADAS s.r.l.

SAP

Teradata

Initial release

2013

1983

host version

2006

2010

1984

Current release

5.1, August 2022

12.1, October 2016

8.0

2.0 SPS07 (April 4, 2023), April 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

commercial

free version is available

commercial

free trial version available

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C and C++

C++

Server operating systems

Linux

AIX
HP-UX
Linux
Solaris
Windows
z/OS

AIX
Linux
Windows

Appliance or cloud-service

hosted
Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

.Net
C
C#
C++
Groovy
Java
PHP
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

SQLScript, R

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

horizontal partitioning

yes

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

with separate tools (MQ, InfoSphere)

none

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID at container level

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

managed by 'Learn by Usage'

yes

User concepts

Access control

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

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

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.