DBMS > Google BigQuery vs. IBM Db2 vs. Linter vs. Percona Server for MongoDB vs. Warp 10

System Properties Comparison Google BigQuery vs. IBM Db2 vs. Linter vs. Percona Server for MongoDB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Common in IBM host environments, 2 different versions for host and Windows/Linux

RDBMS for high security requirements

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Document store

Time Series DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

cloud.google.com/bigquery

www.ibm.com/products/db2

linter.ru

www.percona.com/mongodb/software/percona-server-for-mongodb

www.warp10.io

Technical documentation

cloud.google.com/bigquery/docs

www.ibm.com/docs/en/db2

docs.percona.com/percona-distribution-for-mongodb

www.warp10.io/content/02_Getting_started

Developer

Google

IBM

relex.ru

Percona

SenX

Initial release

2010

1983

host version

1990

2015

Current release

12.1, October 2016

3.4.10-2.10, November 2017

License

Commercial or Open Source

commercial

free version is available

commercial

Open Source

GPL Version 2

Open Source

Apache License 2.0

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

C and C++

C++

Java

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

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

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

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

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

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

proprietary protocol using JSON

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

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

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax with the possibility to convert from PL/SQL

JavaScript

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

only with Windows/Unix/Linux Version

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

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

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

via In-Memory Engine

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users and roles

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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