DBMS > IBM Db2 warehouse vs. Sequoiadb vs. Spark SQL vs. Transbase

System Properties Comparison IBM Db2 warehouse vs. Sequoiadb vs. Spark SQL vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

Cloud-based data warehousing service

NewSQL database with distributed OLTP and SQL

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

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

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

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

Score	1.86
Rank	#129	Overall
	#63	Relational DBMS

Score	0.42
Rank	#256	Overall
	#39	Document stores
	#120	Relational DBMS

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Score	0.07
Rank	#325	Overall
	#147	Relational DBMS

Website

www.ibm.com/products/db2-warehouse

www.sequoiadb.com

spark.apache.org/sql

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

Technical documentation

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

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

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

Developer

IBM

Sequoiadb Ltd.

Apache Software Foundation

Transaction Software GmbH

Initial release

2014

2013

2014

1987

Current release

3.2.0, October 2021

Transbase 8.4, September 2020

License

Commercial or Open Source

commercial

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache 2.0

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

C++

Scala

C and C++

Server operating systems

hosted

Linux

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

yes

SQL-like query language

SQL-like DML and DDL statements

yes

APIs and other access methods

.NET Client API
JDBC
ODBC
OLE DB

proprietary protocol using JSON

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

.Net
C++
Java
PHP
Python

Java
Python
R
Scala

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

Server-side scripts

Stored procedures

PL/SQL, SQL PL

JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

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

Eventual Consistency

Immediate 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

yes

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

fine grained access rights according to SQL-standard

simple password-based access control

fine grained access rights according to SQL-standard

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