DBMS > Amazon DynamoDB vs. BigObject vs. HEAVY.AI vs. Kdb vs. Kinetica

System Properties Comparison Amazon DynamoDB vs. BigObject vs. HEAVY.AI vs. Kdb vs. Kinetica

Editorial information provided by DB-Engines

Name

Description

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

Analytic DBMS for real-time computations and queries

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

High performance Time Series DBMS

Fully vectorized database across both GPUs and CPUs

Primary database model

Document store
Key-value store

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Time Series DBMS
Vector DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Relational DBMS

Spatial DBMS
Time Series DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Website

aws.amazon.com/dynamodb

bigobject.io

github.com/heavyai/heavydb
www.heavy.ai

kx.com

www.kinetica.com

Technical documentation

docs.aws.amazon.com/dynamodb

Developer

Amazon

BigObject, Inc.

HEAVY.AI, Inc.

Kx Systems, a division of First Derivatives plc

Kinetica

Initial release

2012

2015

2016

2000

kdb was released 2000, kdb+ in 2003

2012

Current release

5.10, January 2022

3.6, May 2018

7.1, August 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free community edition available

Open Source

Apache Version 2; enterprise edition available

commercial

free 32-bit version

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

C++ and CUDA

C, C++

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

Linux
OS X
Solaris
Windows

Linux

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

table attribute 'grouped'

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language (q)

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

user defined functions

Triggers

yes

by integration with AWS Lambda

yes

with views

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Round robin

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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)

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

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

rights management via user accounts

Access rights for users and roles on table level

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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