DBMS > DolphinDB vs. Google BigQuery vs. IBM Cloudant vs. Linter vs. Microsoft Azure Cosmos DB

System Properties Comparison DolphinDB vs. Google BigQuery vs. IBM Cloudant vs. Linter vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Large scale data warehouse service with append-only tables

Database as a Service offering based on Apache CouchDB

RDBMS for high security requirements

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Relational DBMS

Spatial DBMS

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

Score	4.23
Rank	#82	Overall
	#6	Time Series DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	2.73
Rank	#108	Overall
	#20	Document stores

Score	0.10
Rank	#350	Overall
	#153	Relational DBMS

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

www.dolphindb.com

cloud.google.com/bigquery

www.ibm.com/products/cloudant

linter.ru

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.dolphindb.cn/en/help200/index.html

cloud.google.com/bigquery/docs

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

Developer

DolphinDB, Inc

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

relex.ru

Microsoft

Initial release

2018

2010

1990

2014

Current release

v2.00.4, January 2022

License

Commercial or Open Source

commercial

free community version 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

C++

Erlang

C and C++

Server operating systems

Linux
Windows

hosted

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

APIs and other access methods

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

RESTful HTTP/JSON API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

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

Supported programming languages

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

View functions (Map-Reduce) in JavaScript

yes

proprietary syntax with the possibility to convert from PL/SQL

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

yes

Since BigQuery is designed for querying data

atomic operations within a document possible

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

Administrators, Users, Groups

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

Google Cloud Identity & Access Management (IAM)

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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