DBMS > Amazon Redshift vs. Databricks vs. Firebase Realtime Database vs. GridGain vs. Linter

System Properties Comparison Amazon Redshift vs. Databricks vs. Firebase Realtime Database vs. GridGain vs. Linter

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

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

RDBMS for high security requirements

Primary database model

Relational DBMS

Document store
Relational DBMS

Document store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Website

aws.amazon.com/redshift

www.databricks.com

firebase.google.com/products/realtime-database

www.gridgain.com

linter.ru

Technical documentation

docs.aws.amazon.com/redshift

docs.databricks.com

firebase.google.com/docs/database

www.gridgain.com/docs/index.html

Developer

Amazon (based on PostgreSQL)

Databricks

Google

acquired by Google 2014

GridGain Systems, Inc.

relex.ru

Initial release

2012

2013

2012

2007

1990

Current release

GridGain 8.5.1

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

C and C++

Server operating systems

hosted

Linux
OS X
Solaris
Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

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

with Databricks SQL

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

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

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

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

Java
JavaScript
Objective-C

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

limited functionality with using 'rules'

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

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

Callbacks are triggered when data changes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

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

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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