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

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

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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

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

High-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.

Primary database model

Relational DBMS

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

Time Series DBMS

Relational DBMS

Secondary database models

Document store
RDF store

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

with Db2 Spatial Extender

Relational DBMS

Document store
Wide column store

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	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	2.91
Rank	#102	Overall
	#51	Relational DBMS

Website

cloud.google.com/bigquery

www.ibm.com/products/db2

www.timescale.com

www.yugabyte.com

Technical documentation

cloud.google.com/bigquery/docs

www.ibm.com/docs/en/db2

docs.timescale.com

docs.yugabyte.com
github.com/yugabyte/yugabyte-db

Developer

Google

IBM

Timescale

Yugabyte Inc.

Initial release

2010

1983

host version

2017

Current release

12.1, October 2016

2.15.0, May 2024

2.19, September 2023

License

Commercial or Open Source

commercial

free version is available

Open Source

Apache 2.0

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.

YugabyteDB Managed is the fully managed database-as-a-service offering of YugabyteDB. Get started quickly, and effortlessly ensure continuous availability and limitless scale of your cloud native applications.

Implementation language

C and C++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
OS X
Windows

Linux
OS X

Data scheme

yes

depending on used data model

Typing

predefined data types such as float or date

yes

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

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

yes

SQL

Support of SQL

yes

full PostgreSQL SQL syntax

yes, PostgreSQL compatible

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

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

JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL

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++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
Scala

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

yes

sql, plpgsql, C

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

only with Windows/Unix/Linux Version

yes, across time and space (hash partitioning) attributes

Hash and Range Sharding, row-level geo-partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

Source-replica replication with hot standby and reads on replicas

Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Strong consistency on writes and tunable consistency on reads

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

Distributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

based on RocksDB

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)

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

Specific characteristics

YugabyteDB is an open source distributed SQL database for cloud native transactional...
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Competitive advantages

PostgreSQL compatible: Get instantly productive with a PostgreSQL compatible RDBMS....
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Typical application scenarios

Systems of record and engagement for cloud native applications that require resilience,...
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Market metrics

2 Million+ lifetime clusters deployed, 6.5K+ GitHub stars, 7K YugabyteDB Community...
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Licensing and pricing models

Apache 2.0 license for the database
» more

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