DBMS > Machbase Neo vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. SAP Adaptive Server vs. SAP SQL Anywhere

System Properties Comparison Machbase Neo vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. SAP Adaptive Server vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

TimeSeries DBMS for AIoT and BigData

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Wide column 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	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	35.09
Rank	#25	Overall
	#17	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

machbase.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

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

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

machbase.com/dbms

learn.microsoft.com/azure/cosmos-db

help.sap.com/docs/SAP_ASE

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Machbase

Microsoft

SAP, Sybase

SAP

formerly Sybase

Initial release

2013

2014

2012

1987

1992

Current release

V8.0, August 2023

16.0

17, July 2015

License

Commercial or Open Source

commercial

free test version available

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++

Server operating systems

Linux
macOS
Windows

hosted

AIX
HP-UX
Linux
Solaris
Unix
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API

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

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Cobol
Java
Java
Perl
PHP
Python

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

JavaScript

Java and Transact-SQL

yes, in C/C++, Java, .Net or Perl

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication
Source-replica replication

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

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

volatile and lookup table

yes

User concepts

Access control

simple password-based access control

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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