DBMS > Google BigQuery vs. Infobright vs. MySQL vs. TigerGraph

System Properties Comparison Google BigQuery vs. Infobright vs. MySQL vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Widely used open source RDBMS

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Relational DBMS

Key/Value like access via memcached API

Graph DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Website

cloud.google.com/bigquery

ignitetech.com/softwarelibrary/infobrightdb

www.mysql.com

www.tigergraph.com

Technical documentation

cloud.google.com/bigquery/docs

dev.mysql.com/doc

docs.tigergraph.com

Developer

Google

Ignite Technologies Inc.; formerly InfoBright Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2010

2005

1995

2017

Current release

8.4.0, April 2024

License

Commercial or Open Source

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

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.

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Implementation language

C and C++

C++

Server operating systems

hosted

Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

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

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language (GSQL)

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Relationships in graphs

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

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

exploiting MySQL or PostgreSQL frontend capabilities

Users with fine-grained authorization concept

no user groups or roles

Role-based access control

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