DBMS > DolphinDB vs. Google BigQuery vs. Microsoft Azure Table Storage vs. Teradata vs. Warp 10

System Properties Comparison DolphinDB vs. Google BigQuery vs. Microsoft Azure Table Storage vs. Teradata vs. Warp 10

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

A Wide Column Store for rapid development using massive semi-structured datasets

A hybrid cloud data analytics software platform (Teradata Vantage)

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

www.dolphindb.com

cloud.google.com/bigquery

azure.microsoft.com/en-us/services/storage/tables

www.teradata.com

www.warp10.io

Technical documentation

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

cloud.google.com/bigquery/docs

docs.teradata.com

www.warp10.io/content/02_Getting_started

Developer

DolphinDB, Inc

Google

Microsoft

Teradata

SenX

Initial release

2018

2010

2012

1984

2015

Current release

v2.00.4, January 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

free community version available

commercial

Open Source

Apache License 2.0

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++

Java

Server operating systems

Linux
Windows

hosted

hosted
Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes

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

SQL

Support of SQL

SQL-like query language

yes

SQL 2016 + extensions

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

RESTful HTTP API

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

HTTP API
Jupyter
WebSocket

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes

UDFs, stored procedures, table functions in parallel

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

Implicit feature of the cloud service

Sharding

Hashing

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

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 based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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