DBMS > Geode vs. Kinetica vs. Microsoft Azure Table Storage vs. SAP HANA vs. SpaceTime

System Properties Comparison Geode vs. Kinetica vs. Microsoft Azure Table Storage vs. SAP HANA vs. SpaceTime

Editorial information provided by DB-Engines

Name

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Fully vectorized database across both GPUs and CPUs

A Wide Column Store for rapid development using massive semi-structured datasets

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

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Relational DBMS

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

Score	1.86
Rank	#134	Overall
	#24	Key-value stores

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Website

geode.apache.org

www.kinetica.com

azure.microsoft.com/en-us/services/storage/tables

www.sap.com/products/hana.html

www.mireo.com/spacetime

Technical documentation

geode.apache.org/docs

docs.kinetica.com

help.sap.com/hana

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Kinetica

Microsoft

SAP

Mireo

Initial release

2002

2012

2010

2020

Current release

1.1, February 2017

7.1, August 2021

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

Cloud-based only

Only available as a cloud service

yes

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++

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

Linux

hosted

Appliance or cloud-service

Linux

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (OQL)

SQL-like DML and DDL statements

yes

A subset of ANSI SQL is implemented

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

JDBC
ODBC

RESTful HTTP API

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

C++
Java
JavaScript (Node.js)
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Python

Server-side scripts

Stored procedures

user defined functions

SQLScript, R

Triggers

yes

Cache Event Listeners

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

Implicit feature of the cloud service

yes

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes, on a single node

optimistic locking

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 per client and object definable

Access rights for users and roles on table level

Access rights based on private key authentication or shared access signatures

yes

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