DBMS > LeanXcale vs. Riak TS vs. Teradata vs. Teradata Aster

System Properties Comparison LeanXcale vs. Riak TS vs. Teradata vs. Teradata Aster

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Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

A hybrid cloud data analytics software platform (Teradata Vantage)

Platform for big data analytics on multistructured data sources and types

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

www.leanxcale.com

www.teradata.com

Technical documentation

www.tiot.jp/riak-docs/riak/ts/latest

docs.teradata.com

Developer

LeanXcale

Open Source, formerly Basho Technologies

Teradata

Initial release

2015

1984

2005

Current release

3.0.0, September 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

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

Erlang

Server operating systems

Linux
OS X

hosted
Linux

Linux

Data scheme

yes

schema-free

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

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

restricted

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

yes

SQL

Support of SQL

yes

through Apache Derby

yes, limited

yes

SQL 2016 + extensions

yes

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

HTTP API
Native Erlang Interface

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
Java
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

Erlang

yes

UDFs, stored procedures, table functions in parallel

R packages

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

links between datasets can be stored

yes

Transaction concepts

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

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

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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2 April 2013, Paul Andlinger

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