DBMS > Blueflood vs. Microsoft SQL Server vs. Netezza vs. Splice Machine

System Properties Comparison Blueflood vs. Microsoft SQL Server vs. Netezza vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Microsofts flagship relational DBMS

Data warehouse and analytics appliance part of IBM PureSystems

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

blueflood.io

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

www.ibm.com/products/netezza

splicemachine.com

Technical documentation

github.com/rax-maas/blueflood/wiki

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

splicemachine.com/how-it-works

Developer

Rackspace

Microsoft

IBM

Splice Machine

Initial release

2013

1989

2000

2014

Current release

SQL Server 2022, November 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

Java

Server operating systems

Linux
OS X

Linux
Windows

Linux

included in appliance

Linux
OS X
Solaris
Windows

Data scheme

predefined scheme

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

yes

APIs and other access methods

HTTP REST

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

JDBC
ODBC
OLE DB

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

C
C++
Fortran
Java
Lua
Perl
Python
R

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

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

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

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

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes, but depending on the SQL-Server Edition

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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, multi-version concurrency control (MVCC)

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

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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

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