DBMS > Amazon Neptune vs. FatDB vs. Geode vs. Vertica

System Properties Comparison Amazon Neptune vs. FatDB vs. Geode vs. Vertica

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Fast, reliable graph database built for the cloud

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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

Graph DBMS
RDF store

Document store
Key-value store

Key-value store

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	2.20
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	1.51
Rank	#147	Overall
	#25	Key-value stores

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

aws.amazon.com/neptune

geode.apache.org

www.vertica.com

Technical documentation

aws.amazon.com/neptune/developer-resources

geode.apache.org/docs

vertica.com/documentation

Developer

Amazon

FatCloud

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2017

2012

2002

2005

Current release

1.1, February 2017

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available as Gemfire

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

hosted

Windows

All OS with a Java VM

the JDK (8 or later) is also required

Linux

Data scheme

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

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

yes

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

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like query language (OQL)

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

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

Java Client API
Memcached protocol
RESTful HTTP API

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

character-based, interactive, front-end utility

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
All JVM based languages
C++
Groovy
Java
Scala

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

Server-side scripts

Stored procedures

yes

via applications

user defined functions

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

via applications

yes

Cache Event Listeners

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

selectable replication factor

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Can implement custom security layer via applications

Access rights per client and object definable

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

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