DBMS > Amazon Aurora vs. Google BigQuery vs. Ignite vs. Machbase Neo vs. MySQL

System Properties Comparison Amazon Aurora vs. Google BigQuery vs. Ignite vs. Machbase Neo vs. MySQL

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Large scale data warehouse service with append-only tables

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.

TimeSeries DBMS for AIoT and BigData

Widely used open source RDBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	7.57
Rank	#51	Overall
	#32	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

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

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

aws.amazon.com/rds/aurora

cloud.google.com/bigquery

ignite.apache.org

machbase.com

www.mysql.com

Technical documentation

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

cloud.google.com/bigquery/docs

apacheignite.readme.io/docs

machbase.com/dbms

dev.mysql.com/doc

Developer

Amazon

Google

Apache Software Foundation

Machbase

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2015

2010

2015

2013

1995

Current release

Apache Ignite 2.6

V8.0, August 2023

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free test version available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java, .Net

C and C++

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

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

yes

SQL

Support of SQL

yes

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

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

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

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

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

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

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

yes

proprietary syntax

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes (replicated cache)

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

volatile and lookup table

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

simple password-based access control

Users with fine-grained authorization concept

no user groups or roles

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