DBMS > Google BigQuery vs. GridGain vs. IBM Cloudant vs. InfinityDB vs. JaguarDB

System Properties Comparison Google BigQuery vs. GridGain vs. IBM Cloudant vs. InfinityDB vs. JaguarDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

GridGain is an in-memory computing platform, built on Apache Ignite

Database as a Service offering based on Apache CouchDB

A Java embedded Key-Value Store which extends the Java Map interface

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Document store

Key-value store

Key-value store
Vector DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Website

cloud.google.com/bigquery

www.gridgain.com

www.ibm.com/products/cloudant

boilerbay.com

www.jaguardb.com

Technical documentation

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

cloud.ibm.com/docs/Cloudant

boilerbay.com/infinitydb/manual

www.jaguardb.com/support.html

Developer

Google

GridGain Systems, Inc.

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Boiler Bay Inc.

DataJaguar, Inc.

Initial release

2010

2007

2010

2002

2015

Current release

GridGain 8.5.1

4.0

3.3 July 2023

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Erlang

Java

C++

the server part. Clients available in other languages

Server operating systems

hosted

Linux
OS X
Solaris
Windows

hosted

All OS with a Java VM

Linux

Data scheme

yes

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

RESTful HTTP/JSON API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C#
Java
JavaScript
Objective-C
PHP
Ruby

Java

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

View functions (Map-Reduce) in JavaScript

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication
Source-replica replication

none

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Eventual Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

atomic operations within a document possible

ACID

Optimistic locking for transactions; no isolation for bulk loads

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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)

Security Hooks for custom implementations

Access rights for users can be defined per database

rights management via user accounts

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