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

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

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Cloud-based data warehousing service

Scalable, distributed, serverless MySQL database platform built on top of Vitess

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

Primary database model

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

cloud.google.com/bigquery

www.ibm.com/products/db2/warehouse

planetscale.com

sqream.com

Technical documentation

cloud.google.com/bigquery/docs

planetscale.com/docs

docs.sqream.com

Developer

Google

IBM

PlanetScale

SQream Technologies

Initial release

2010

2014

2020

2017

Current release

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

Docker
Linux
macOS

Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

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

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
OLE DB

ADO.NET
JDBC
MySQL protocol
ODBC

.Net
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

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

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

PL/SQL, SQL PL

yes

proprietary syntax

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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 across shards
Immediate Consistency within a shard

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

ACID at shard level

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

Users with fine-grained authorization concept

no user groups or roles

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