DBMS > Alibaba Cloud Table Store vs. Amazon DynamoDB vs. Citus vs. Datomic vs. Teradata Aster

System Properties Comparison Alibaba Cloud Table Store vs. Amazon DynamoDB vs. Citus vs. Datomic vs. Teradata Aster

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 fully managed Wide Column Store for large quantities of semi-structured data with real-time access

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

Platform for big data analytics on multistructured data sources and types

Primary database model

Wide column store

Document store
Key-value store

Relational DBMS

Secondary database models

Document store

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Website

www.alibabacloud.com/product/table-store

aws.amazon.com/dynamodb

www.citusdata.com

www.datomic.com

Technical documentation

www.alibabacloud.com/help/en/tablestore

docs.aws.amazon.com/dynamodb

docs.citusdata.com

docs.datomic.com

Developer

Alibaba

Amazon

Cognitect

Teradata

Initial release

2016

2012

2010

2012

2005

Current release

8.1, December 2018

1.0.6735, June 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

AGPL, commercial license also available

commercial

limited edition free

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, Clojure

Server operating systems

hosted

Linux

All OS with a Java VM

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

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

specific XML type available, but no XML query functionality

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

yes

APIs and other access methods

HTTP API

RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Java
Python

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Clojure
Java

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

Transaction Functions

R packages

Triggers

yes

by integration with AWS Lambda

yes

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

none

But extensive use of caching in the application peers

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Source-replica replication

other methods possible by using 3rd party extensions

none

But extensive use of caching in the application peers

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

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

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 single-row operations

ACID

ACID across one or more tables within a single AWS account and region

ACID

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

Access rights based on subaccounts and tokens

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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