DBMS > Google BigQuery vs. Hive vs. IBM Db2 vs. VoltDB

System Properties Comparison Google BigQuery vs. Hive vs. IBM Db2 vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

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

Relational DBMS

Secondary database models

Document store
RDF store

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

with Db2 Spatial Extender

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

cloud.google.com/bigquery

hive.apache.org

www.ibm.com/products/db2

www.voltdb.com

Technical documentation

cloud.google.com/bigquery/docs

cwiki.apache.org/confluence/display/Hive/Home

www.ibm.com/docs/en/db2

docs.voltdb.com

Developer

Google

Apache Software Foundation

initially developed by Facebook

IBM

VoltDB Inc.

Initial release

2010

2012

1983

host version

2010

Current release

3.1.3, April 2022

12.1, October 2016

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

free version is available

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Java

C and C++

Java, C++

Server operating systems

hosted

All OS with a Java VM

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
OS X

for development

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
PHP
Python

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

only with Windows/Unix/Linux Version

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

with separate tools (MQ, InfoSphere)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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