DBMS > atoti vs. IBM Db2 warehouse vs. Spark SQL vs. SWC-DB vs. Transbase

System Properties Comparison atoti vs. IBM Db2 warehouse vs. Spark SQL vs. SWC-DB vs. Transbase

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Cloud-based data warehousing service

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

A high performance, scalable Wide Column DBMS

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.59
Rank	#242	Overall
	#10	Object oriented DBMS

Score	1.34
Rank	#166	Overall
	#76	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.01
Rank	#387	Overall
	#13	Wide column stores

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

atoti.io

www.ibm.com/products/db2/warehouse

spark.apache.org/sql

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

www.transaction.de/en/products/transbase.html

Technical documentation

docs.atoti.io

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

www.transaction.de/en/products/transbase/features.html

Developer

ActiveViam

IBM

Apache Software Foundation

Alex Kashirin

Transaction Software GmbH

Initial release

2014

2020

1987

Current release

3.5.0 ( 2.13), September 2023

0.5, April 2021

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

Apache 2.0

Open Source

GPL V3

commercial

free development license

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Scala

C++

C and C++

Server operating systems

hosted

Linux
OS X
Windows

Linux

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

.NET Client API
JDBC
ODBC
OLE DB

JDBC
ODBC

Proprietary protocol
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Java
Python
R
Scala

C++

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

Python

PL/SQL, SQL PL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

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