DBMS > Google BigQuery vs. IBM Db2 vs. Quasardb vs. TimescaleDB

System Properties Comparison Google BigQuery vs. IBM Db2 vs. Quasardb vs. TimescaleDB

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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

Distributed, high-performance timeseries database

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Relational DBMS

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

Time Series DBMS

Secondary database models

Document store
RDF store

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

with Db2 Spatial Extender

Relational DBMS

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

Score	50.03
Rank	#24	Overall
	#16	Relational DBMS

Score	157.23
Rank	#7	Overall
	#5	Relational DBMS

Score	0.07
Rank	#330	Overall
	#29	Time Series DBMS

Score	4.79
Rank	#81	Overall
	#5	Time Series DBMS

Website

cloud.google.com/bigquery

www.ibm.com/analytics/db2

quasardb.net

www.timescale.com

Technical documentation

cloud.google.com/bigquery/docs

www.ibm.com/support/knowledgecenter/SSEPGG

doc.quasar.ai/master

docs.timescale.com

Developer

Google

IBM

quasardb

Timescale

Initial release

2010

1983

host version

2009

2017

Current release

12.1, October 2016

3.13.3, April 2022

2.6.0, February 2022

License

Commercial or Open Source

commercial

free version is available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache 2.0

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++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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.

yes

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

yes

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

only with Windows/Unix/Linux Version

Sharding

consistent hashing

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

Source-replica replication with selectable replication factor

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

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

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Timescale is the creator of TimescaleDB , the first open-source relational database...
» more

Competitive advantages

TimescaleDB is purpose-built to scale and handle time-series data workloads and is...
» more

Typical application scenarios

Time-series Analytics TimescaleDB boosts the performance of PostgreSQL for time-series...
» more

Key customers

TransferWise, Maersk, Walmart, Comcast, LaunchDarkly, IBM, NOV, Cisco, Nutanix, Warner...
» more

Market metrics

With over 12,700 GitHub stars and growing, 1,000,000 active databases, and an active...
» more

Licensing and pricing models

TimescaleDB is available as a self-hosted product under two licenses, the Timescale...
» more

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