DBMS > Atos Standard Common Repository vs. LeanXcale vs. SAP HANA vs. Transwarp Hippo

System Properties Comparison Atos Standard Common Repository vs. LeanXcale vs. SAP HANA vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Document store
Key-value store

Key-value store
Relational DBMS

Relational DBMS

Vector DBMS

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	0.28
Rank	#286	Overall
	#41	Key-value stores
	#130	Relational DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Score	0.00
Rank	#385	Overall
	#16	Vector DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.leanxcale.com

www.sap.com/products/hana.html

www.transwarp.cn/en/subproduct/hippo

Technical documentation

help.sap.com/hana

Developer

Atos Convergence Creators

LeanXcale

SAP

Initial release

2016

2015

2010

2023

Current release

1703

2.0 SPS07 (April 4, 2023), April 2023

1.0, May 2023

License

Commercial or Open Source

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

C++

Server operating systems

Linux

Appliance or cloud-service

Linux
macOS

Data scheme

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

optional

yes

Vector, Numeric and String

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

through Apache Derby

yes

APIs and other access methods

LDAP

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JDBC
ODBC

RESTful HTTP API

Supported programming languages

All languages with LDAP bindings

C
Java
Scala

C++
Java
Python

Server-side scripts

Stored procedures

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

yes

Sharding

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 or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

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

LDAP bind authentication

yes

Role based access control and fine grained access rights

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