DBMS > Amazon Aurora vs. Bangdb vs. HEAVY.AI vs. Kinetica vs. Microsoft SQL Server

System Properties Comparison Amazon Aurora vs. Bangdb vs. HEAVY.AI vs. Kinetica vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Fully vectorized database across both GPUs and CPUs

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Document store

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	7.91
Rank	#50	Overall
	#32	Relational DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

aws.amazon.com/rds/aurora

bangdb.com

github.com/heavyai/heavydb
www.heavy.ai

www.kinetica.com

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

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

docs.bangdb.com

docs.heavy.ai

docs.kinetica.com

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

Developer

Amazon

Sachin Sinha, BangDB

HEAVY.AI, Inc.

Kinetica

Microsoft

Initial release

2015

2012

2016

2012

1989

Current release

BangDB 2.0, October 2021

5.10, January 2022

7.1, August 2021

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

BSD 3

Open Source

Apache Version 2; enterprise edition available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

C, C++

C++ and CUDA

C, C++

C++

Server operating systems

hosted

Linux

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

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

yes

SQL like support with command line tool

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C#
C++
Java
Python

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes

user defined functions

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

Triggers

yes

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

horizontal partitioning

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

Sharding

Round robin

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

Multi-source replication

Source-replica replication

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

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Immediate Consistency

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

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

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

yes, run db with in-memory only mode

yes

GPU vRAM or System RAM

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes (enterprise version only)

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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