DBMS > BigchainDB vs. GigaSpaces vs. Google BigQuery vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage

System Properties Comparison BigchainDB vs. GigaSpaces vs. Google BigQuery vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Large scale data warehouse service with append-only tables

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Document store

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Secondary database models

Graph DBMS
Search engine

Spatial DBMS

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Website

www.bigchaindb.com

www.gigaspaces.com

cloud.google.com/bigquery

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

learn.microsoft.com/azure/cosmos-db

Developer

Gigaspaces Technologies

Google

Microsoft

Initial release

2016

2000

2010

2014

2012

Current release

15.5, September 2020

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

Apache Version 2; Commercial licenses available

commercial

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

Python

Java, C++, .Net

Server operating systems

Linux

Linux
macOS
Solaris
Windows

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON 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.

XML can be used for describing objects metadata

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-99 for query and DML statements

yes

SQL-like query language

APIs and other access methods

CLI Client
RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

RESTful HTTP/JSON API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

JavaScript

Triggers

yes, event driven architecture

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

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

yes

Role-based access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

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