DBMS > IBM Db2 Event Store vs. SAP HANA vs. SAP IQ vs. Spark SQL

System Properties Comparison IBM Db2 Event Store vs. SAP HANA vs. SAP IQ vs. Spark SQL

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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

Columnar RDBMS optimized for Big Data analytics

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

Primary database model

Event Store
Time Series DBMS

Relational DBMS

column-oriented

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	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	2.64
Rank	#108	Overall
	#52	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

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

www.sap.com/products/hana.html

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

spark.apache.org/sql

Technical documentation

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

help.sap.com/hana

help.sap.com/docs/SAP_IQ

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

Developer

IBM

SAP

SAP, formerly Sybase

Apache Software Foundation

Initial release

2017

2010

1994

2014

Current release

2.0

2.0 SPS07 (April 4, 2023), April 2023

16.1 SPS04, April 2019

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free developer edition available

commercial

Open Source

Apache 2.0

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++

Scala

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Appliance or cloud-service

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
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.

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

Supported programming languages

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

SQLScript, R

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

yes

SAP/Sybase Replication Server

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

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 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

fine grained access rights according to SQL-standard

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