DBMS > Bangdb vs. Kinetica vs. MaxDB vs. Microsoft Azure Table Storage vs. Microsoft SQL Server

System Properties Comparison Bangdb vs. Kinetica vs. MaxDB vs. Microsoft Azure Table Storage vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

Fully vectorized database across both GPUs and CPUs

A robust and reliable RDBMS optimized to run all major SAP solutions

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

Microsofts flagship relational DBMS

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

bangdb.com

www.kinetica.com

maxdb.sap.com

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

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

Technical documentation

docs.bangdb.com

docs.kinetica.com

maxdb.sap.com/documentation

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

Developer

Sachin Sinha, BangDB

Kinetica

SAP, acquired from Software AG (Adabas-D) in 1997

Microsoft

Initial release

2012

1984

2012

1989

Current release

BangDB 2.0, October 2021

7.1, August 2021

7.9.10.12, February 2024

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

BSD 3

commercial

Limited community edition free

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C, C++

C++

Server operating systems

Linux

AIX
HP-UX
Linux
Solaris
Windows

hosted

Linux
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like DML and DDL statements

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
WebDAV

RESTful HTTP API

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

Supported programming languages

C
C#
C++
Java
Python

C++
Java
JavaScript (Node.js)
Python

.Net
C#
Java
Perl
PHP
Python

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

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

Server-side scripts

Stored procedures

user defined functions

yes

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

Triggers

yes, Notifications (with Streaming only)

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

none

Sharding

Implicit feature of the cloud service

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication

yes

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

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, run db with in-memory only mode

yes

GPU vRAM or System RAM

yes

User concepts

Access control

yes (enterprise version only)

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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