DBMS > IBM Db2 Event Store vs. Ignite vs. Microsoft SQL Server vs. SiriDB vs. TerminusDB

System Properties Comparison IBM Db2 Event Store vs. Ignite vs. Microsoft SQL Server vs. SiriDB vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

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.

Microsofts flagship relational DBMS

Open Source Time Series DBMS

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Event Store
Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Graph DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
RDF store

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

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

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

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

ignite.apache.org

www.microsoft.com/en-us/sql-server

siridb.com

terminusdb.com

Technical documentation

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

apacheignite.readme.io/docs

learn.microsoft.com/en-US/sql/sql-server

docs.siridb.com

terminusdb.github.io/terminusdb/#

Developer

IBM

Apache Software Foundation

Microsoft

Cesbit

DataChemist Ltd.

Initial release

2017

2015

1989

2017

2018

Current release

2.0

Apache Ignite 2.6

SQL Server 2022, November 2022

11.0.0, January 2023

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

MIT License

Open Source

GPL V3

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

C and C++

C++, Java, .Net

C++

Prolog, Rust

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Solaris
Windows

Linux
Windows

Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data

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

yes

through the embedded Spark runtime

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

yes

SQL-like query language (WOQL)

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

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

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

JavaScript
Python

Server-side scripts

Stored procedures

yes

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

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

yes (replicated cache)

yes, but depending on the SQL-Server Edition

yes

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

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

No - written data is immutable

yes

Durability

Support for making data persistent

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

yes

in-memory journaling

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

simple rights management via user accounts

Role-based access control

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