DBMS > Ignite vs. ReductStore vs. SAP HANA vs. SAP IQ

System Properties Comparison Ignite vs. ReductStore vs. SAP HANA vs. SAP IQ

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Editorial information provided by DB-Engines

Name

Description

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

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

Columnar RDBMS optimized for Big Data analytics

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

column-oriented

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	2.64
Rank	#108	Overall
	#52	Relational DBMS

Website

ignite.apache.org

github.com/reductstore
www.reduct.store

www.sap.com/products/hana.html

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

Technical documentation

apacheignite.readme.io/docs

www.reduct.store/docs

help.sap.com/hana

help.sap.com/docs/SAP_IQ

Developer

Apache Software Foundation

ReductStore LLC

SAP

SAP, formerly Sybase

Initial release

2015

2023

2010

1994

Current release

Apache Ignite 2.6

1.9, March 2024

2.0 SPS07 (April 4, 2023), April 2023

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache 2.0

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

C++, Java, .Net

C++, Rust

Server operating systems

Linux
OS X
Solaris
Windows

Docker
Linux
macOS
Windows

Appliance or cloud-service

AIX
HP-UX
Linux
Solaris
Windows

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.

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C++
JavaScript (Node.js)
Python
Rust

C
C#
C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

SQLScript, R

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

shared disk or shared nothing architectures with SAP IQ Multiplexer

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

SAP/Sybase Replication Server

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

User concepts

Access control

Security Hooks for custom implementations

yes

fine grained access rights according to SQL-standard

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