DBMS > GridGain vs. InterSystems Caché vs. Netezza vs. Oracle vs. Spark SQL

System Properties Comparison GridGain vs. InterSystems Caché vs. Netezza vs. Oracle vs. Spark SQL

Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

GridGain is an in-memory computing platform, built on Apache Ignite

A multi-model DBMS and application server

Data warehouse and analytics appliance part of IBM PureSystems

Widely used RDBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Key-value store
Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.gridgain.com

www.intersystems.com/products/cache

www.ibm.com/products/netezza

www.oracle.com/database

spark.apache.org/sql

Technical documentation

www.gridgain.com/docs/index.html

docs.intersystems.com

docs.oracle.com/en/database

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

GridGain Systems, Inc.

InterSystems

IBM

Oracle

Apache Software Foundation

Initial release

2007

1997

2000

1980

2014

Current release

GridGain 8.5.1

2018.1.4, May 2020

23c, September 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache 2.0

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

Java, C++, .Net

C and C++

Scala

Server operating systems

Linux
OS X
Solaris
Windows

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Linux

included in appliance

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
OS X
Windows

Data scheme

yes

depending on used data model

yes

Schemaless in JSON and XML columns

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

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

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

.NET Client API
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
OLE DB

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C#
C++
Java

C
C++
Fortran
Java
Lua
Perl
Python
R

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Java
Python
R
Scala

Server-side scripts

Stored procedures

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

yes

PL/SQL

also stored procedures in Java possible

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Sharding, horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Source-replica replication

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

can be realized in PL/SQL

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

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Security Hooks for custom implementations

Access rights for users, groups and roles

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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