DBMS > Alibaba Cloud MaxCompute vs. IBM Db2 Event Store vs. Microsoft SQL Server vs. RDF4J vs. Valentina Server

System Properties Comparison Alibaba Cloud MaxCompute vs. IBM Db2 Event Store vs. Microsoft SQL Server vs. RDF4J vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

MaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousing

Distributed Event Store optimized for Internet of Things use cases

Microsofts flagship relational DBMS

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Object-relational database and reports server

Primary database model

Relational DBMS

a graph-processing framework is available with the graphs being stored in tables

Event Store
Time Series DBMS

Relational DBMS

RDF store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

www.alibabacloud.com/product/maxcompute

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

www.microsoft.com/en-us/sql-server

rdf4j.org

www.valentina-db.net

Technical documentation

www.alibabacloud.com/help/en/maxcompute

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

learn.microsoft.com/en-US/sql/sql-server

rdf4j.org/documentation

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Alibaba

IBM

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Paradigma Software

Initial release

2016

2017

1989

2004

1999

Current release

2.0

SQL Server 2022, November 2022

5.7.5

License

Commercial or Open Source

commercial

free developer edition available

commercial

restricted free version is available

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C and C++

C++

Java

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux
Windows

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

RDF Schemas

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

through the embedded Spark runtime

yes

APIs and other access methods

Fluentd
Flume
MaxCompute Console

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ODBC

Supported programming languages

Java

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Java
PHP
Python

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

in Java

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Active-active shard replication

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

in-memory storage is supported as well

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 and roles

fine grained access rights according to SQL-standard

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