DBMS > BigObject vs. Kinetica vs. MaxDB vs. ReductStore

System Properties Comparison BigObject vs. Kinetica vs. MaxDB vs. ReductStore

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Fully vectorized database across both GPUs and CPUs

A robust and reliable RDBMS optimized to run all major SAP solutions

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	2.26
Rank	#114	Overall
	#55	Relational DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Website

bigobject.io

www.kinetica.com

maxdb.sap.com

github.com/reductstore
www.reduct.store

Technical documentation

docs.bigobject.io

docs.kinetica.com

maxdb.sap.com/documentation

www.reduct.store/docs

Developer

BigObject, Inc.

Kinetica

SAP, acquired from Software AG (Adabas-D) in 1997

ReductStore LLC

Initial release

2015

2012

1984

2023

Current release

7.1, August 2021

7.9.10.12, February 2024

1.9, March 2024

License

Commercial or Open Source

commercial

free community edition available

commercial

Limited community edition free

Open Source

Business Source License 1.1

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++

C++

C++, Rust

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

AIX
HP-UX
Linux
Solaris
Windows

Docker
Linux
macOS
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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
WebDAV

HTTP API

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

.Net
C#
Java
Perl
PHP
Python

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

Lua

user defined functions

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

GPU vRAM or System RAM

User concepts

Access control

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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