DBMS > Atos Standard Common Repository vs. Databricks vs. SAP HANA vs. Yaacomo

System Properties Comparison Atos Standard Common Repository vs. Databricks vs. SAP HANA vs. Yaacomo

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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.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store
Key-value store

Document store
Relational DBMS

Relational 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	84.24
Rank	#14	Overall
	#2	Document stores
	#9	Relational DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Website

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

www.databricks.com

www.sap.com/products/hana.html

yaacomo.com

Technical documentation

docs.databricks.com

help.sap.com/hana

Developer

Atos Convergence Creators

Databricks

SAP

Q2WEB GmbH

Initial release

2016

2013

2010

2009

Current release

1703

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

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

Server operating systems

Linux

hosted

Appliance or cloud-service

Android
Linux
Windows

Data scheme

Schema and schema-less with LDAP views

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

optional

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

with Databricks SQL

yes

APIs and other access methods

LDAP

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

Python
R
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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