DBMS > Amazon DynamoDB vs. Amazon Redshift vs. Kinetica vs. Spark SQL vs. Tibero

System Properties Comparison Amazon DynamoDB vs. Amazon Redshift vs. Kinetica vs. Spark SQL vs. Tibero

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service for use with business intelligence tools

Fully vectorized database across both GPUs and CPUs

Spark SQL is a component on top of 'Spark Core' for structured data processing

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Document store
Key-value store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

aws.amazon.com/dynamodb

aws.amazon.com/redshift

www.kinetica.com

spark.apache.org/sql

us.tmaxsoft.com/products/tibero

Technical documentation

docs.aws.amazon.com/dynamodb

docs.aws.amazon.com/redshift

docs.kinetica.com

spark.apache.org/docs/latest/sql-programming-guide.html

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Amazon

Amazon (based on PostgreSQL)

Kinetica

Apache Software Foundation

TmaxSoft

Initial release

2012

2014

2003

Current release

7.1, August 2021

3.5.0 ( 2.13), September 2023

6, April 2015

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Scala

C and Assembler

Server operating systems

hosted

Linux

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

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

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

Java
Python
R
Scala

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Persistent Stored Procedure (PSM)

Triggers

yes

by integration with AWS Lambda

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

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

In-memory capabilities

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

yes

GPU vRAM or System RAM

planned for next version

User concepts

Access control

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

Access rights for users and roles on table level

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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