DBMS > Amazon Redshift vs. EsgynDB vs. Firebase Realtime Database vs. Google Cloud Bigtable vs. GridGain

System Properties Comparison Amazon Redshift vs. EsgynDB vs. Firebase Realtime Database vs. Google Cloud Bigtable vs. GridGain

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

GridGain is an in-memory computing platform, built on Apache Ignite

Primary database model

Relational DBMS

Document store

Key-value store
Wide column store

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Website

aws.amazon.com/redshift

www.esgyn.cn

firebase.google.com/products/realtime-database

cloud.google.com/bigtable

www.gridgain.com

Technical documentation

docs.aws.amazon.com/redshift

firebase.google.com/docs/database

cloud.google.com/bigtable/docs

www.gridgain.com/docs/index.html

Developer

Amazon (based on PostgreSQL)

Esgyn

Google

acquired by Google 2014

Google

GridGain Systems, Inc.

Initial release

2012

2015

2012

2015

2007

Current release

GridGain 8.5.1

License

Commercial or Open Source

commercial

commercial, open source

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++, Java

Java, C++, .Net, Python, REST, SQL

Server operating systems

hosted

Linux

hosted

Linux
OS X
Solaris
Windows
z/OS

Data scheme

yes

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

Android
iOS
JavaScript API
RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/ADO.Net

Java
JavaScript
Objective-C

C#
C++
Go
Java
JavaScript (Node.js)
Python

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures

limited functionality with using 'rules'

yes (compute grid and cache interceptors can be used instead)

Triggers

Callbacks are triggered when data changes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

Internal replication in Colossus, and regional replication between two clusters in different zones

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

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

yes

Atomic single-row operations

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Role-based access control
Security Hooks for custom implementations

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