DBMS > Hive vs. Kinetica vs. Linter vs. SAP HANA

System Properties Comparison Hive vs. Kinetica vs. Linter vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Fully vectorized database across both GPUs and CPUs

RDBMS for high security requirements

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

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

hive.apache.org

www.kinetica.com

linter.ru

www.sap.com/products/hana.html

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

docs.kinetica.com

help.sap.com/hana

Developer

Apache Software Foundation

initially developed by Facebook

Kinetica

relex.ru

SAP

Initial release

2012

1990

2010

Current release

3.1.3, April 2022

7.1, August 2021

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2

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

Java

C, C++

C and C++

Server operating systems

All OS with a Java VM

Linux

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Appliance or cloud-service

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

JDBC
ODBC
Thrift

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

JDBC
ODBC

Supported programming languages

C++
Java
PHP
Python

C++
Java
JavaScript (Node.js)
Python

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

yes

proprietary syntax with the possibility to convert from PL/SQL

SQLScript, R

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

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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, groups and roles

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

yes

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