DBMS > Amazon DynamoDB vs. BigObject vs. Drizzle vs. EsgynDB vs. Kinetica

System Properties Comparison Amazon DynamoDB vs. BigObject vs. Drizzle vs. EsgynDB vs. Kinetica

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

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

Analytic DBMS for real-time computations and queries

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

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	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Website

aws.amazon.com/dynamodb

bigobject.io

www.esgyn.cn

www.kinetica.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.bigobject.io

docs.kinetica.com

Developer

Amazon

BigObject, Inc.

Drizzle project, originally started by Brian Aker

Esgyn

Kinetica

Initial release

2012

2015

2008

2015

2012

Current release

7.2.4, September 2012

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

GNU GPL

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++

C++, Java

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)

FreeBSD
Linux
OS X

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

JDBC

ADO.NET
JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C++
Java
PHP

All languages supporting JDBC/ODBC/ADO.Net

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

Java Stored Procedures

user defined functions

Triggers

yes

by integration with AWS Lambda

hooks for callbacks inside the server can be used.

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

Multi-source replication between multi datacenters

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

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

ACID

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)

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

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