DBMS > Google BigQuery vs. Ignite vs. Linter vs. Microsoft Access vs. SiteWhere

System Properties Comparison Google BigQuery vs. Ignite vs. Linter vs. Microsoft Access vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

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.

RDBMS for high security requirements

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

cloud.google.com/bigquery

ignite.apache.org

linter.ru

www.microsoft.com/en-us/microsoft-365/access

github.com/sitewhere/sitewhere

Technical documentation

cloud.google.com/bigquery/docs

apacheignite.readme.io/docs

developer.microsoft.com/en-us/access

sitewhere1.sitewhere.io/index.html

Developer

Google

Apache Software Foundation

relex.ru

Microsoft

SiteWhere

Initial release

2010

2015

1990

1992

2010

Current release

Apache Ignite 2.6

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Bundled with Microsoft Office

Open Source

Common Public Attribution License Version 1.0

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++

C++

Java

Server operating systems

hosted

Linux
OS X
Solaris
Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Windows

Not a real database server, but making use of DLLs

Linux
OS X
Windows

Data scheme

yes

predefined scheme

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

yes

but not compliant to any SQL standard

APIs and other access methods

RESTful HTTP/JSON API

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

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

ADO.NET
DAO
ODBC
OLE DB

HTTP REST

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

yes

proprietary syntax with the possibility to convert from PL/SQL

yes

since Access 2010 using the ACE-engine

Triggers

yes (cache interceptors and events)

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Source-replica replication

none

selectable replication factor

based on HBase

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

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

but no files for transaction logging

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

a simple user-level security was built in till version Access 2003

Users with fine-grained authorization concept

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