DBMS > BigchainDB vs. GeoSpock vs. Microsoft Azure Cosmos DB vs. Teradata vs. Tibero

System Properties Comparison BigchainDB vs. GeoSpock vs. Microsoft Azure Cosmos DB vs. Teradata vs. Tibero

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

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

Spatial and temporal data processing engine for extreme data scale

Globally distributed, horizontally scalable, multi-model database service

A hybrid cloud data analytics software platform (Teradata Vantage)

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

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

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Website

www.bigchaindb.com

geospock.com

azure.microsoft.com/services/cosmos-db

www.teradata.com

us.tmaxsoft.com/products/tibero

Technical documentation

bigchaindb.readthedocs.io/en/latest

learn.microsoft.com/azure/cosmos-db

docs.teradata.com

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

GeoSpock

Microsoft

Teradata

TmaxSoft

Initial release

2016

2014

1984

2003

Current release

2.0, September 2019

Teradata Vantage 1.0 MU2, January 2019

6, April 2015

License

Commercial or Open Source

Open Source

AGPL v3

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, Javascript

C and Assembler

Server operating systems

Linux

hosted

hosted
Linux

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

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.

yes

Secondary indexes

temporal, categorical

yes

All properties auto-indexed by default

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

SQL-like query language

yes

SQL 2016 + extensions

yes

APIs and other access methods

CLI Client
RESTful HTTP API

JDBC

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

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

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

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

JavaScript

yes

UDFs, stored procedures, table functions in parallel

Persistent Stored Procedure (PSM)

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

Implicit feature of the cloud service

Sharding

Hashing

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

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

planned for next version

User concepts

Access control

yes

Access rights for users can be defined per table

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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