DBMS > Apache IoTDB vs. BigchainDB vs. dBASE vs. GBase vs. Microsoft Azure Cosmos DB

System Properties Comparison Apache IoTDB vs. BigchainDB vs. dBASE vs. GBase vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

dBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Time Series DBMS

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	10.34
Rank	#44	Overall
	#28	Relational DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

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

Website

azure.microsoft.com/services/cosmos-db

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

bigchaindb.readthedocs.io/en/latest

www.dbase.com/support/knowledgebase

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

Asthon Tate

General Data Technology Co., Ltd.

Microsoft

Initial release

2018

2016

1979

2004

2014

Current release

1.1.0, April 2023

dBASE 2019, 2019

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL v3

commercial

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

Python

C, Java, Python

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

DOS

dBase Classic
Windows

dBase Pro

Linux

hosted

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

Standard with numerous extensions

SQL-like query language

APIs and other access methods

JDBC
Native API

CLI Client
RESTful HTTP API

none

The IDE can access other DBMS or ODBC-sources.

ADO.NET
C API
JDBC
ODBC

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Go
Haskell
Java
JavaScript
Python
Ruby

dBase proprietary IDE

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

Server-side scripts

Stored procedures

yes

The IDE can access stored procedures in other database systems.

user defined functions

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

none

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

selectable replication factor

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

not for dBase internal data, but IDE does support transactions when accessing external DBMS

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

Access rights for users and roles

yes

Access rights can be defined down to the item level

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