DBMS > Kinetica vs. Microsoft SQL Server vs. ObjectBox vs. Oracle Berkeley DB vs. Teradata

System Properties Comparison Kinetica vs. Microsoft SQL Server vs. ObjectBox vs. Oracle Berkeley DB vs. Teradata

Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

Microsofts flagship relational DBMS

Extremely fast embedded database for small devices, IoT and Mobile

Widely used in-process key-value store

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Object oriented DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

www.kinetica.com

www.microsoft.com/en-us/sql-server

objectbox.io

www.oracle.com/database/technologies/related/berkeleydb.html

www.teradata.com

Technical documentation

docs.kinetica.com

learn.microsoft.com/en-US/sql/sql-server

docs.objectbox.io

docs.oracle.com/cd/E17076_05/html/index.html

docs.teradata.com

Developer

Kinetica

Microsoft

ObjectBox Limited

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Teradata

Initial release

2012

1989

2017

1994

1984

Current release

7.1, August 2021

SQL Server 2022, November 2022

18.1.40, May 2020

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache License 2.0

Open Source

commercial license available

commercial

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 and C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux

Linux
Windows

Android
iOS
Linux
macOS
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

hosted
Linux

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.

yes

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL interfaced based on SQLite is available

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Proprietary native API

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

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

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

Server-side scripts

Stored procedures

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

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

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, but depending on the SQL-Server Edition

online/offline synchronization between client and server

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

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

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

Vector Database Release for Flutter / Dart + Python
13 June 2023

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