DBMS > Amazon DynamoDB vs. Blueflood vs. HEAVY.AI vs. jBASE vs. Kinetica

System Properties Comparison Amazon DynamoDB vs. Blueflood vs. HEAVY.AI vs. jBASE vs. Kinetica

Editorial information provided by DB-Engines

Name

Description

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

Scalable TimeSeries DBMS based on Cassandra

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

A robust multi-value DBMS comprising development tools and middleware

Fully vectorized database across both GPUs and CPUs

Primary database model

Document store
Key-value store

Secondary database models

Spatial DBMS

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	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Website

aws.amazon.com/dynamodb

blueflood.io

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

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.kinetica.com

Technical documentation

docs.aws.amazon.com/dynamodb

github.com/rax-maas/blueflood/wiki

docs.heavy.ai

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

docs.kinetica.com

Developer

Amazon

Rackspace

HEAVY.AI, Inc.

Rocket Software (formerly Zumasys)

Kinetica

Initial release

2012

2013

2016

1991

2012

Current release

5.10, January 2022

5.7

7.1, August 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

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

C++ and CUDA

C, C++

Server operating systems

hosted

Linux
OS X

Linux

AIX
Linux
Windows

Linux

Data scheme

schema-free

predefined scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

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

SQL

Support of SQL

yes

Embedded SQL for jBASE in BASIC

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

HTTP REST

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

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

.Net
Basic
Jabbascript
Java

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

by integration with AWS Lambda

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

based on Cassandra

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Multi-source replication

yes

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

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

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 can be defined down to the item level

Access rights for users and roles on table level

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