DBMS > Google BigQuery vs. IBM Cloudant vs. IBM Db2 Event Store vs. ToroDB vs. Warp 10

System Properties Comparison Google BigQuery vs. IBM Cloudant vs. IBM Db2 Event Store vs. ToroDB vs. Warp 10

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Large scale data warehouse service with append-only tables

Database as a Service offering based on Apache CouchDB

Distributed Event Store optimized for Internet of Things use cases

A MongoDB-compatible JSON document store, built on top of PostgreSQL

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Document store

Event Store
Time Series DBMS

Document store

Time Series DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

cloud.google.com/bigquery

www.ibm.com/products/cloudant

www.ibm.com/products/db2-event-store

github.com/torodb/server

www.warp10.io

Technical documentation

cloud.google.com/bigquery/docs

cloud.ibm.com/docs/Cloudant

www.ibm.com/docs/en/db2-event-store

www.warp10.io/content/02_Getting_started

Developer

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

IBM

8Kdata

SenX

Initial release

2010

2017

2016

2015

Current release

2.0

License

Commercial or Open Source

commercial

free developer edition available

Open Source

AGPL-V3

Open Source

Apache License 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

Erlang

C and C++

Java

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

All OS with a Java 7 VM

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

through the embedded Spark runtime

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in JavaScript

View functions (Map-Reduce) in JavaScript

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Active-active shard replication

Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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)

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

Access rights for users and roles

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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