DBMS > Amazon Redshift vs. Hive vs. Ignite vs. ObjectBox vs. RavenDB

System Properties Comparison Amazon Redshift vs. Hive vs. Ignite vs. ObjectBox vs. RavenDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Extremely fast embedded database for small devices, IoT and Mobile

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Object oriented DBMS

Document store

Secondary database models

Time Series DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

cwiki.apache.org/confluence/display/Hive/Home

apacheignite.readme.io/docs

docs.objectbox.io

ravendb.net/docs

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

initially developed by Facebook

Apache Software Foundation

ObjectBox Limited

Hibernating Rhinos

Initial release

2012

2015

2017

2010

Current release

3.1.3, April 2022

Apache Ignite 2.6

5.4, July 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Apache License 2.0

Open Source

AGPL version 3, commercial license available

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

Java

C++, Java, .Net

C and C++

Server operating systems

hosted

All OS with a Java VM

Linux
OS X
Solaris
Windows

Android
iOS
Linux
macOS
Windows

Linux
macOS
Raspberry Pi
Windows

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

SQL-like DML and DDL statements

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

SQL-like query language (RQL)

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift

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

Proprietary native API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
PHP
Python

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

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

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes (replicated cache)

online/offline synchronization between client and server

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

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

ACID, Cluster-wide transaction available

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

Access rights for users, groups and roles

Security Hooks for custom implementations

yes

Authorization levels configured per client per database

More information provided by the system vendor

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