DBMS > Google BigQuery vs. IBM Db2 vs. IBM Db2 warehouse vs. KeyDB vs. SQream DB

System Properties Comparison Google BigQuery vs. IBM Db2 vs. IBM Db2 warehouse vs. KeyDB vs. SQream DB

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

Cloud-based data warehousing service

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

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

Relational DBMS

Key-value store

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	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

cloud.google.com/bigquery

www.ibm.com/products/db2

www.ibm.com/products/db2/warehouse

github.com/Snapchat/KeyDB
keydb.dev

sqream.com

Technical documentation

cloud.google.com/bigquery/docs

www.ibm.com/docs/en/db2

docs.keydb.dev

docs.sqream.com

Developer

Google

IBM

EQ Alpha Technology Ltd.

SQream Technologies

Initial release

2010

1983

host version

2014

2019

2017

Current release

12.1, October 2016

2022.1.6, December 2022

License

Commercial or Open Source

commercial

free version is available

commercial

Open Source

BSD-3

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.

Implementation language

C and C++

C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

hosted

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

yes, ANSI Standard SQL Types

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.

Import/export of XML data possible

Secondary indexes

yes

by using the Redis Search module

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

.NET Client API
JDBC
ODBC
OLE DB

Proprietary protocol

RESP - REdis Serialization Protoco

.Net
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

PL/SQL, SQL PL

Lua

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

only with Windows/Unix/Linux Version

Sharding

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

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

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

simple password-based access control and ACL

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