DBMS > atoti vs. DataFS vs. LeanXcale vs. SAP HANA

System Properties Comparison atoti vs. DataFS vs. LeanXcale vs. SAP HANA

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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.

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

Primary database model

Object oriented DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Graph DBMS

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	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

atoti.io

newdatabase.com

www.leanxcale.com

www.sap.com/products/hana.html

Technical documentation

docs.atoti.io

dev.mobiland.com/Overview.xsp

help.sap.com/hana

Developer

ActiveViam

Mobiland AG

LeanXcale

SAP

Initial release

2018

2015

2010

Current release

1.1.263, October 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

free versions available

commercial

Cloud-based only

Only available as a cloud service

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

Server operating systems

Windows

Appliance or cloud-service

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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.

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

through Apache Derby

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

C
Java
Scala

Server-side scripts

Stored procedures

Python

SQLScript, R

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Proprietary Sharding system

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

ACID

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

Windows-Profile

yes

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