DBMS > EXASOL vs. SAP HANA vs. Spark SQL vs. SWC-DB

System Properties Comparison EXASOL vs. SAP HANA vs. Spark SQL vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

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

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Wide column store

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Time Series DBMS

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

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

www.exasol.com

www.sap.com/products/hana.html

spark.apache.org/sql

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

www.exasol.com/resources

help.sap.com/hana

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

Developer

Exasol

SAP

Apache Software Foundation

Alex Kashirin

Initial release

2000

2010

2014

2020

Current release

2.0 SPS07 (April 4, 2023), April 2023

3.5.0 ( 2.13), September 2023

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL V3

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

Scala

C++

Server operating systems

Appliance or cloud-service

Linux
OS X
Windows

Linux

Data scheme

yes

schema-free

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

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC

Proprietary protocol
Thrift

Supported programming languages

Java
Lua
Python
R

Java
Python
R
Scala

C++

Server-side scripts

Stored procedures

user defined functions

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

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

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

yes

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