DBMS > Graph Engine vs. Kinetica vs. ReductStore vs. SAP HANA

System Properties Comparison Graph Engine vs. Kinetica vs. ReductStore vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Fully vectorized database across both GPUs and CPUs

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.

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

Primary database model

Graph DBMS
Key-value store

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS
Time Series 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	0.56
Rank	#241	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Website

www.graphengine.io

www.kinetica.com

github.com/reductstore
www.reduct.store

www.sap.com/products/hana.html

Technical documentation

www.graphengine.io/docs/manual

docs.kinetica.com

www.reduct.store/docs

help.sap.com/hana

Developer

Microsoft

Kinetica

ReductStore LLC

SAP

Initial release

2010

2012

2023

2010

Current release

7.1, August 2021

1.9, March 2024

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

Business Source License 1.1

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

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Implementation language

.NET and C

C, C++

C++, Rust

Server operating systems

.NET

Linux

Docker
Linux
macOS
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

RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

HTTP API

JDBC
ODBC

Supported programming languages

C#
C++
F#
Visual Basic

C++
Java
JavaScript (Node.js)
Python

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

yes

user defined functions

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

horizontal partitioning

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

yes

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