DBMS > Amazon Aurora vs. FatDB vs. Google BigQuery vs. Hazelcast vs. MarkLogic

System Properties Comparison Amazon Aurora vs. FatDB vs. Google BigQuery vs. Hazelcast vs. MarkLogic

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

MySQL and PostgreSQL compatible cloud service by Amazon

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

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

Operational and transactional Enterprise NoSQL database

Primary database model

Relational DBMS

Document store
Key-value store

Relational DBMS

Key-value store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

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

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Website

aws.amazon.com/rds/aurora

cloud.google.com/bigquery

hazelcast.com

www.marklogic.com

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

docs.marklogic.com

Developer

Amazon

FatCloud

Google

Hazelcast

MarkLogic Corp.

Initial release

2015

2012

2010

2008

2001

Current release

5.3.6, November 2023

11.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

restricted free version is 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++

Server operating systems

hosted

Windows

hosted

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Schema can be enforced

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

yes

Via inetgration in SQL Server

yes

SQL-like query language

yes

SQL92

APIs and other access methods

ADO.NET
JDBC
ODBC

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

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

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

Server-side scripts

Stored procedures

yes

via applications

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

yes

via XQuery or JavaScript

Triggers

yes

via applications

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes

Replicated Map

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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

ACID

can act as a resource manager in an XA/JTA transaction

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

yes, with Range Indexes

User concepts

Access control

fine grained access rights according to SQL-standard

Can implement custom security layer via applications

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Role-based access control

Role-based access control at the document and subdocument levels

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