DBMS > IBM Cloudant vs. SAP Adaptive Server vs. Spark SQL vs. Yaacomo

System Properties Comparison IBM Cloudant vs. SAP Adaptive Server vs. Spark SQL vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Database as a Service offering based on Apache CouchDB

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store

Relational DBMS

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	35.09
Rank	#25	Overall
	#17	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.ibm.com/products/cloudant

www.sap.com/products/technology-platform/sybase-ase.html

spark.apache.org/sql

yaacomo.com

Technical documentation

cloud.ibm.com/docs/Cloudant

help.sap.com/docs/SAP_ASE

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

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

SAP, Sybase

Apache Software Foundation

Q2WEB GmbH

Initial release

2010

1987

2014

2009

Current release

16.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

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

Erlang

C and C++

Scala

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Unix
Windows

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C++
Cobol
Java
Java
Perl
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

Java and Transact-SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

yes, utilizing Spark Core

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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 can be defined per database

fine grained access rights according to SQL-standard

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