DBMS > atoti vs. FatDB vs. Google Cloud Datastore vs. SAP HANA vs. searchxml

System Properties Comparison atoti vs. FatDB vs. Google Cloud Datastore vs. SAP HANA vs. searchxml

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

In-memory, column based data store. Available as appliance or cloud service

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Object oriented DBMS

Document store
Key-value store

Document store

Relational DBMS

Native XML DBMS
Search engine

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
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	44.27
Rank	#23	Overall
	#16	Relational DBMS

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

Website

atoti.io

cloud.google.com/datastore

www.sap.com/products/hana.html

www.searchxml.net/category/products

Technical documentation

docs.atoti.io

cloud.google.com/datastore/docs

help.sap.com/hana

www.searchxml.net/support/handouts

Developer

ActiveViam

FatCloud

Google

SAP

informationpartners gmbh

Initial release

2012

2008

2010

2015

Current release

2.0 SPS07 (April 4, 2023), April 2023

1.0

License

Commercial or Open Source

commercial

free versions available

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

Windows

hosted

Appliance or cloud-service

Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

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)

Via inetgration in SQL Server

SQL-like query language (GQL)

yes

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

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

JDBC
ODBC

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

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

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

Python

yes

via applications

using Google App Engine

SQLScript, R

yes

on the application server

Triggers

yes

via applications

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication using Paxos

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

Eventual Consistency
Immediate Consistency

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

Can implement custom security layer via applications

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

yes

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.