DBMS > Amazon Redshift vs. Hazelcast vs. Kinetica vs. NCache vs. Titan

System Properties Comparison Amazon Redshift vs. Hazelcast vs. Kinetica vs. NCache vs. Titan

Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

Large scale data warehouse service for use with business intelligence tools

A widely adopted in-memory data grid

Fully vectorized database across both GPUs and CPUs

Open-Source and Enterprise in-memory Key-Value Store

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS
Time Series DBMS

Document store
Search engine

Using distributed Lucene

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Website

aws.amazon.com/redshift

hazelcast.com

www.kinetica.com

www.alachisoft.com/ncache

github.com/thinkaurelius/titan

Technical documentation

docs.aws.amazon.com/redshift

hazelcast.org/imdg/docs

docs.kinetica.com

www.alachisoft.com/resources/docs

github.com/thinkaurelius/titan/wiki

Developer

Amazon (based on PostgreSQL)

Hazelcast

Kinetica

Alachisoft

Aurelius, owned by DataStax

Initial release

2012

2008

2012

2005

2012

Current release

5.3.6, November 2023

7.1, August 2021

5.3.3, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Enterprise Edition available

Open Source

Apache license, version 2.0

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, C++

C#, .NET, .NET Core, Java

Java

Server operating systems

hosted

All OS with a Java VM

Linux

Linux
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

the object must implement a serialization strategy

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

SQL-like DML and DDL statements

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

APIs and other access methods

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

IDistributedCache
JCache
LINQ
Proprietary native API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

All languages supporting JDBC/ODBC

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

C++
Java
JavaScript (Node.js)
Python

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Event Listeners, Executor Services

user defined functions

support for stored procedures with SQL-Server CLR

yes

Triggers

yes

Events

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Notifications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Source-replica replication

yes, with selectable consistency level

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Relationships in graph

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

optimistic locking and pessimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

GPU vRAM or System RAM

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

Access rights for users and roles on table level

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

User authentification and security via Rexster Graph Server

More information provided by the system vendor

Specific characteristics

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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