DBMS > H2GIS vs. Heroic vs. Teradata Aster

System Properties Comparison H2GIS vs. Heroic 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

Spatial extension of H2

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Platform for big data analytics on multistructured data sources and types

Primary database model

Spatial DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

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Trend Chart

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Website

www.h2gis.org

github.com/spotify/heroic

Technical documentation

www.h2gis.org/docs/home

spotify.github.io/heroic

Developer

CNRS

Spotify

Teradata

Initial release

2013

2014

2005

License

Commercial or Open Source

Open Source

LGPL 3.0

Open Source

Apache 2.0

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

Server operating systems

Linux

Data scheme

yes

schema-free

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

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Java

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

based on H2

R packages

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

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

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

yes

based on H2

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

Mellanox InfiniBand Helps Accelerate Teradata Aster Big Analytics Appliance
1 May 2024, AOL

Teradata Aster Analytics Going Places: On Hadoop and AWS
24 August 2016, PR Newswire

Northwestern Analytics Partners with Teradata Aster to Host Hackathon
23 May 2014, Northwestern Engineering

Teradata Aster gets graph database, HDFS-compatible file store
8 October 2013, ZDNet

Teradata's Aster shows how the flowers of fraud bloom
23 April 2015, The Register

provided by Google News

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