DBMS > IBM Db2 Event Store vs. SAP HANA vs. Sequoiadb

System Properties Comparison IBM Db2 Event Store vs. SAP HANA vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

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

NewSQL database with distributed OLTP and SQL

Primary database model

Event Store
Time Series DBMS

Relational DBMS

Document store
Relational DBMS

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

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Website

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

www.sap.com/products/hana.html

www.sequoiadb.com

Technical documentation

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

help.sap.com/hana

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

IBM

SAP

Sequoiadb Ltd.

Initial release

2017

2010

2013

Current release

2.0

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

free developer edition available

commercial

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

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

C and C++

C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Appliance or cloud-service

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

yes

through the embedded Spark runtime

yes

SQL-like query language

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

proprietary protocol using JSON

Supported programming languages

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

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

SQLScript, R

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Document is locked during a transaction

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 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

yes

simple password-based access control

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