DBMS > Coveo vs. H2 vs. Microsoft SQL Server vs. Netezza vs. Spark SQL

System Properties Comparison Coveo vs. H2 vs. Microsoft SQL Server vs. Netezza vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Microsofts flagship relational DBMS

Data warehouse and analytics appliance part of IBM PureSystems

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	2.11
Rank	#118	Overall
	#11	Search engines

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.coveo.com

www.h2database.com

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

www.ibm.com/products/netezza

spark.apache.org/sql

Technical documentation

docs.coveo.com

www.h2database.com/html/main.html

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

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Coveo

Thomas Mueller

Microsoft

IBM

Apache Software Foundation

Initial release

2012

2005

1989

2000

2014

Current release

2.2.220, July 2023

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

restricted free version is available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Scala

Server operating systems

hosted

All OS with a Java VM

Linux
Windows

Linux

included in appliance

Linux
OS X
Windows

Data scheme

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

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

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

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

JDBC
ODBC
OLE DB

JDBC
ODBC

Supported programming languages

C#
Java
JavaScript
Python

Java

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

C
C++
Fortran
Java
Lua
Perl
Python
R

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

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

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

none

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

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

With clustering: 2 database servers on different computers operate on identical copies of a database

yes, but depending on the SQL-Server Edition

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

granular access controls, API key management, content filters

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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