DBMS > Amazon Redshift vs. Datomic vs. NCache vs. NSDb vs. Vertica

System Properties Comparison Amazon Redshift vs. Datomic vs. NCache vs. NSDb vs. Vertica

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Document store
Search engine

Using distributed Lucene

Spatial DBMS
Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

aws.amazon.com/redshift

www.datomic.com

www.alachisoft.com/ncache

nsdb.io

www.vertica.com

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

www.alachisoft.com/resources/docs

nsdb.io/Architecture

vertica.com/documentation

Developer

Amazon (based on PostgreSQL)

Cognitect

Alachisoft

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2005

2017

2005

Current release

1.0.6735, June 2023

5.3.3, April 2024

12.0.3, January 2023

License

Commercial or Open Source

commercial

limited edition free

Open Source

Enterprise Edition available

Open Source

Apache Version 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

C#, .NET, .NET Core, Java

Java, Scala

C++

Server operating systems

hosted

All OS with a Java VM

Linux
Windows

Linux
macOS

Linux

Data scheme

yes

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

partial

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

yes: int, bigint, decimal, string

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.

Secondary indexes

restricted

yes

all fields are automatically indexed

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

does not fully support an SQL-standard

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

SQL-like query language

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

IDistributedCache
JCache
LINQ
Proprietary native API

gRPC
HTTP REST
WebSocket

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

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

Java
Scala

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

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

support for stored procedures with SQL-Server CLR

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

By using transaction functions

yes

Notifications

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

yes

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

yes, with selectable consistency level

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Transaction concepts

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

ACID

optimistic locking and pessimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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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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