DBMS > Amazon Redshift vs. Badger vs. BigObject vs. EJDB

System Properties Comparison Amazon Redshift vs. Badger vs. BigObject vs. EJDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Analytic DBMS for real-time computations and queries

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Primary database model

Relational DBMS

Key-value store

Relational DBMS

a hierachical model (tree) can be imposed

Document store

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.31
Rank	#296	Overall
	#44	Document stores

Website

aws.amazon.com/redshift

github.com/dgraph-io/badger

bigobject.io

github.com/Softmotions/ejdb

Technical documentation

docs.aws.amazon.com/redshift

godoc.org/github.com/dgraph-io/badger

docs.bigobject.io

github.com/Softmotions/ejdb/blob/master/README.md

Developer

Amazon (based on PostgreSQL)

DGraph Labs

BigObject, Inc.

Softmotions

Initial release

2012

2017

2015

2012

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free community edition available

Open Source

GPLv2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

server-less

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

in-process shared library

Supported programming languages

All languages supporting JDBC/ODBC

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Read/Write Locking

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

User concepts

Access control

fine grained access rights according to SQL-standard

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