DBMS > BigObject vs. Blueflood vs. Google Cloud Datastore vs. Microsoft Azure Cosmos DB vs. RDFox

System Properties Comparison BigObject vs. Blueflood vs. Google Cloud Datastore vs. Microsoft Azure Cosmos DB vs. RDFox

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Scalable TimeSeries DBMS based on Cassandra

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Globally distributed, horizontally scalable, multi-model database service

High performance knowledge graph and semantic reasoning engine

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS
RDF store

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.23
Rank	#308	Overall
	#25	Graph DBMS
	#14	RDF stores

Website

bigobject.io

blueflood.io

cloud.google.com/datastore

azure.microsoft.com/services/cosmos-db

www.oxfordsemantic.tech

Technical documentation

docs.bigobject.io

github.com/rax-maas/blueflood/wiki

cloud.google.com/datastore/docs

learn.microsoft.com/azure/cosmos-db

docs.oxfordsemantic.tech

Developer

BigObject, Inc.

Rackspace

Google

Microsoft

Oxford Semantic Technologies

Initial release

2015

2013

2008

2014

2017

Current release

6.0, Septermber 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

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

Java

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X

hosted

Linux
macOS
Windows

Data scheme

yes

predefined scheme

schema-free

yes

RDF schemas

Typing

predefined data types such as float or date

yes

yes, details here

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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (GQL)

SQL-like query language

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HTTP REST

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

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

RESTful HTTP API
SPARQL 1.1

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

C
Java

Server-side scripts

Stored procedures

Lua

using Google App Engine

JavaScript

Triggers

Callbacks using the Google Apps Engine

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

Multi-source replication using Paxos

yes

Implicit feature of the cloud service

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights can be defined down to the item level

Roles, resources, and access types

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