DBMS > Google BigQuery vs. HBase vs. Infobright vs. Linter vs. Microsoft SQL Server

System Properties Comparison Google BigQuery vs. HBase vs. Infobright vs. Linter vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Wide-column store based on Apache Hadoop and on concepts of BigTable

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

RDBMS for high security requirements

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

cloud.google.com/bigquery

hbase.apache.org

ignitetech.com/softwarelibrary/infobrightdb

linter.ru

www.microsoft.com/en-us/sql-server

Technical documentation

cloud.google.com/bigquery/docs

hbase.apache.org/book.html

learn.microsoft.com/en-US/sql/sql-server

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Ignite Technologies Inc.; formerly InfoBright Inc.

relex.ru

Microsoft

Initial release

2010

2008

2005

1990

1989

Current release

2.3.4, January 2021

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

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

commercial

restricted free version is 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

Java

C and C++

C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

Linux
Windows

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

Linux
Windows

Data scheme

yes

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

ADO.NET
JDBC
ODBC

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Coprocessors in Java

yes

proprietary syntax with the possibility to convert from PL/SQL

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

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

Single row ACID (across millions of columns)

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

fine grained access rights according to SQL-standard

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