DBMS > atoti vs. Google Cloud Datastore vs. Microsoft SQL Server vs. searchxml vs. Valentina Server

System Properties Comparison atoti vs. Google Cloud Datastore vs. Microsoft SQL Server vs. searchxml vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Microsofts flagship relational DBMS

DBMS for structured and unstructured content wrapped with an application server

Object-relational database and reports server

Primary database model

Object oriented DBMS

Document store

Relational DBMS

Native XML DBMS
Search engine

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

atoti.io

cloud.google.com/datastore

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

www.searchxml.net/category/products

www.valentina-db.net

Technical documentation

docs.atoti.io

cloud.google.com/datastore/docs

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

www.searchxml.net/support/handouts

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

Developer

ActiveViam

Google

Microsoft

informationpartners gmbh

Paradigma Software

Initial release

2008

1989

2015

1999

Current release

SQL Server 2022, November 2022

1.0

5.7.5

License

Commercial or Open Source

commercial

free versions available

commercial

restricted free version is available

commercial

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++

Server operating systems

hosted

Linux
Windows

Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

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

Multidimensional Expressions (MDX)

SQL-like query language (GQL)

yes

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

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

RESTful HTTP API
WebDAV
XQuery
XSLT

ODBC

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

C++

most other programming languages supported via APIs

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

Server-side scripts

Stored procedures

Python

using Google App Engine

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

yes

on the application server

yes

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes, but depending on the SQL-Server Edition

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

Domain, group and role-based access control at the document level and for application services

fine grained access rights according to SQL-standard

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