DBMS > Google BigQuery vs. H2 vs. Microsoft Azure SQL Database vs. NCache vs. Valentina Server

System Properties Comparison Google BigQuery vs. H2 vs. Microsoft Azure SQL Database vs. NCache vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

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

Open-Source and Enterprise in-memory Key-Value Store

Object-relational database and reports server

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

Document store
Search engine

Using distributed Lucene

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	8.22
Rank	#50	Overall
	#32	Relational DBMS

Score	78.40
Rank	#15	Overall
	#10	Relational DBMS

Score	0.98
Rank	#196	Overall
	#29	Key-value stores

Score	0.18
Rank	#329	Overall
	#144	Relational DBMS

Website

cloud.google.com/bigquery

www.h2database.com

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

www.alachisoft.com/ncache

www.valentina-db.net

Technical documentation

cloud.google.com/bigquery/docs

www.h2database.com/html/main.html

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

www.alachisoft.com/resources/docs

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Google

Thomas Mueller

Microsoft

Alachisoft

Paradigma Software

Initial release

2010

2005

2010

2005

1999

Current release

2.2.220, July 2023

V12

5.3.3, April 2024

5.7.5

License

Commercial or Open Source

commercial

Open Source

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

commercial

Open Source

Enterprise Edition available

commercial

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

Java

C++

C#, .NET, .NET Core, Java

Server operating systems

hosted

All OS with a Java VM

hosted

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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 query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET
JDBC
ODBC

IDistributedCache
JCache
LINQ
Proprietary native API

ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

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

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java Stored Procedures and User-Defined Functions

Transact SQL

support for stored procedures with SQL-Server CLR

yes

Triggers

yes

Notifications

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes, with always 3 replicas available

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

optimistic locking and pessimistic locking

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
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Market metrics

Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

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