DBMS > GridGain vs. IBM Db2 Event Store vs. SAP IQ vs. Snowflake vs. SQLite

System Properties Comparison GridGain vs. IBM Db2 Event Store vs. SAP IQ vs. Snowflake vs. SQLite

Editorial information provided by DB-Engines

Name

Description

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

Distributed Event Store optimized for Internet of Things use cases

Columnar RDBMS optimized for Big Data analytics

Cloud-based data warehousing service for structured and semi-structured data

Widely used embeddable, in-process RDBMS

Primary database model

Key-value store
Relational DBMS

Event Store
Time Series DBMS

Relational DBMS

column-oriented

Relational DBMS

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	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.gridgain.com

www.ibm.com/products/db2-event-store

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

www.snowflake.com

www.sqlite.org

Technical documentation

www.gridgain.com/docs/index.html

www.ibm.com/docs/en/db2-event-store

help.sap.com/docs/SAP_IQ

docs.snowflake.net/manuals/index.html

www.sqlite.org/docs.html

Developer

GridGain Systems, Inc.

IBM

SAP, formerly Sybase

Snowflake Computing Inc.

Dwayne Richard Hipp

Initial release

2007

2017

1994

2014

2000

Current release

GridGain 8.5.1

2.0

16.1 SPS04, April 2019

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

free developer edition available

commercial

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

C and C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux

Linux, macOS, Windows for the developer addition

AIX
HP-UX
Linux
Solaris
Windows

hosted

server-less

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

through the embedded Spark runtime

yes

SQL-92 is not fully supported

APIs and other access methods

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

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C
C#
C++
Java
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

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

yes

user defined functions

Triggers

yes (cache interceptors and events)

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Active-active shard replication

SAP/Sybase Replication Server

yes

none

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

Eventual Consistency

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

No - written data is immutable

yes

via file-system locks

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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