DBMS > HarperDB vs. Kinetica vs. MariaDB vs. Teradata

System Properties Comparison HarperDB vs. Kinetica vs. MariaDB vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Fully vectorized database across both GPUs and CPUs

MySQL application compatible open source RDBMS, enhanced with high availability, security, interoperability and performance capabilities. MariaDB ColumnStore provides a column-oriented storage engine and MariaDB Xpand supports distributed SQL.

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Document store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS

with OQGraph storage engine
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.55
Rank	#252	Overall
	#38	Document stores

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	93.81
Rank	#13	Overall
	#9	Relational DBMS

Score	47.84
Rank	#21	Overall
	#15	Relational DBMS

Website

www.harperdb.io

www.kinetica.com

mariadb.com

Site of MariaDB Corporation
mariadb.org

Site of MariaDB Foundation

www.teradata.com

Technical documentation

docs.harperdb.io/docs

docs.kinetica.com

mariadb.com/kb/en/library

docs.teradata.com

Developer

HarperDB

Kinetica

MariaDB Corporation Ab (MariaDB Enterprise),
MariaDB Foundation (community MariaDB Server)

The lead developer Monty Widenius is the original author of MySQL

Teradata

Initial release

2017

2012

2009

Fork of MySQL, which was first released in 1995

1984

Current release

3.1, August 2021

7.1, August 2021

11.3.2, February 2024

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

GPL version 2, commercial enterprise subscription 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

Node.js

C, C++

C and C++

Server operating systems

Linux
OS X

Linux

FreeBSD
Linux
Solaris
Windows

ColumnStore storage engine not available on Windows

hosted
Linux

Data scheme

dynamic schema

yes

Dynamic columns are supported

yes

Typing

predefined data types such as float or date

yes

JSON data types

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

SQL-like data manipulation statements

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

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

Supported programming languages

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C++
Java
JavaScript (Node.js)
Python

Ada
C
C#
C++
D
Eiffel
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

user defined functions

yes

PL/SQL compatibility added with version 10.3

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

Partitioning methods

Methods for storing different data on different nodes

A table resides as a whole on one (or more) nodes in a cluster

Sharding

several options for horizontal partitioning and Sharding

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

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

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, using LMDB

yes

not for in-memory storage engine

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

with MEMORY storage engine

yes

User concepts

Access control

Access rights for users and roles

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Web, SaaS and Cloud operational applications that require high availability, scalability...
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Key customers

Deutsche Bank, DBS Bank, Nasdaq, Red Hat, ServiceNow, Verizon and Walgreens Featured...
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Market metrics

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Licensing and pricing models

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