DBMS > Heroic vs. IBM Cloudant vs. RisingWave vs. Teradata Aster

System Properties Comparison Heroic vs. IBM Cloudant vs. RisingWave 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

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

Database as a Service offering based on Apache CouchDB

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Platform for big data analytics on multistructured data sources and types

Primary database model

Time Series DBMS

Document store

Relational DBMS

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

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	2.85
Rank	#95	Overall
	#18	Document stores

Score	0.61
Rank	#237	Overall
	#111	Relational DBMS

Website

github.com/spotify/heroic

www.ibm.com/products/cloudant

www.risingwave.com/database

Technical documentation

spotify.github.io/heroic

cloud.ibm.com/docs/Cloudant

docs.risingwave.com/docs/current/intro

Developer

Spotify

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

RisingWave Labs

Teradata

Initial release

2014

2010

2022

2005

Current release

1.2, September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Erlang

Rust

Server operating systems

hosted

Docker
Linux
macOS

Linux

Data scheme

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

Standard SQL-types and JSON

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

RESTful HTTP/JSON API

JDBC
PostgreSQL wire protocol

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

Go
Java
JavaScript (Node.js)
Python

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

UDFs in Python or Java

R packages

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

Transaction concepts

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

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

Access rights for users can be defined per database

Users and Roles

fine grained access rights according to SQL-standard

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