DBMS > Amazon Redshift vs. Apache Kylin vs. GridGain vs. RocksDB

System Properties Comparison Amazon Redshift vs. Apache Kylin vs. GridGain vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Key-value store

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	1.14
Rank	#171	Overall
	#77	Relational DBMS

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

Score	2.84
Rank	#97	Overall
	#16	Key-value stores

Website

aws.amazon.com/redshift

kylin.apache.org

www.gridgain.com

rocksdb.org

Technical documentation

docs.aws.amazon.com/redshift

kylin.apache.org/docs

www.gridgain.com/docs/index.html

github.com/facebook/rocksdb/wiki

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation, originally contributed from eBay Inc

GridGain Systems, Inc.

Facebook, Inc.

Initial release

2012

2015

2007

2013

Current release

3.1.0, July 2020

GridGain 8.5.1

9.4.0, June 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial, open source

Open Source

BSD

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

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

C++

Server operating systems

hosted

Linux

Linux
OS X
Solaris
Windows
z/OS

Linux

Data scheme

yes

schema-free

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

ANSI SQL for queries (using Apache Calcite)

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

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

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

C++ API
Java API

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

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

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

yes

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

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

yes

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

Role-based access control
Security Hooks for custom implementations

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