DBMS > Atos Standard Common Repository vs. Datomic vs. Splice Machine vs. Teradata Aster

System Properties Comparison Atos Standard Common Repository vs. Datomic vs. Splice Machine vs. Teradata Aster

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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.

Teradata Aster has been integrated into other Teradata systems and therefore 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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Platform for big data analytics on multistructured data sources and types

Primary database model

Document store
Key-value store

Relational DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

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

www.datomic.com

splicemachine.com

Technical documentation

docs.datomic.com

splicemachine.com/how-it-works

Developer

Atos Convergence Creators

Cognitect

Splice Machine

Teradata

Initial release

2016

2012

2014

2005

Current release

1703

1.0.6735, June 2023

3.1, March 2021

License

Commercial or Open Source

commercial

limited edition free

Open Source

AGPL 3.0, commercial license available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Java, Clojure

Java

Server operating systems

Linux

All OS with a Java VM

Linux
OS X
Solaris
Windows

Linux

Data scheme

Schema and schema-less with LDAP views

yes

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

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

LDAP

RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

All languages with LDAP bindings

Clojure
Java

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Java

R packages

Triggers

yes

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

But extensive use of caching in the application peers

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

Multi-source replication
Source-replica replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

LDAP bind authentication

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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