DBMS > BigObject vs. Blueflood vs. Microsoft Azure Cosmos DB vs. RDFox vs. Vertica

System Properties Comparison BigObject vs. Blueflood vs. Microsoft Azure Cosmos DB vs. RDFox vs. Vertica

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Scalable TimeSeries DBMS based on Cassandra

Globally distributed, horizontally scalable, multi-model database service

High performance knowledge graph and semantic reasoning engine

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS
RDF store

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS

Relational DBMS

Spatial DBMS
Time Series 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	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

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

bigobject.io

blueflood.io

azure.microsoft.com/services/cosmos-db

www.oxfordsemantic.tech

www.vertica.com

Technical documentation

docs.bigobject.io

github.com/rax-maas/blueflood/wiki

learn.microsoft.com/azure/cosmos-db

docs.oxfordsemantic.tech

vertica.com/documentation

Developer

BigObject, Inc.

Rackspace

Microsoft

Oxford Semantic Technologies

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2015

2013

2014

2017

2005

Current release

6.0, Septermber 2022

12.0.3, January 2023

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

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

Linux

Data scheme

yes

predefined scheme

schema-free

yes

RDF schemas

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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.

Secondary indexes

yes

All properties auto-indexed by default

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HTTP REST

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

RESTful HTTP API
SPARQL 1.1

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

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

C
Java

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

Lua

JavaScript

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

JavaScript

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

yes

Implicit feature of the cloud service

replication via a shared file system

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

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 can be defined down to the item level

Roles, resources, and access types

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Key customers

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Licensing and pricing models

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