DBMS > BigObject vs. Microsoft Azure SQL Database vs. Sadas Engine vs. Sequoiadb

System Properties Comparison BigObject vs. Microsoft Azure SQL Database vs. Sadas Engine vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Database as a Service offering with high compatibility to Microsoft SQL Server

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Document 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	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

bigobject.io

azure.microsoft.com/en-us/products/azure-sql/database

www.sadasengine.com

www.sequoiadb.com

Technical documentation

docs.bigobject.io

docs.microsoft.com/en-us/azure/azure-sql

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

BigObject, Inc.

Microsoft

SADAS s.r.l.

Sequoiadb Ltd.

Initial release

2015

2010

2006

2013

Current release

V12

8.0

License

Commercial or Open Source

commercial

free community edition available

commercial

free trial version available

Open Source

Server: AGPL; Client: Apache V2

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++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

AIX
Linux
Windows

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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 DML and DDL statements

yes

SQL-like query language

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC
Proprietary protocol

proprietary protocol using JSON

Supported programming languages

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

Lua

Transact SQL

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, with always 3 replicas available

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

simple password-based access control

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