DBMS > BigObject vs. DolphinDB vs. Firebolt vs. Sadas Engine

System Properties Comparison BigObject vs. DolphinDB vs. Firebolt vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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.

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	1.90
Rank	#134	Overall
	#62	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

Technical documentation

docs.bigobject.io

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

docs.firebolt.io

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

BigObject, Inc.

DolphinDB, Inc

Firebolt Analytics Inc.

SADAS s.r.l.

Initial release

2015

2018

2020

2006

Current release

v2.00.4, January 2022

8.0

License

Commercial or Open Source

commercial

free community edition available

commercial

free community version available

commercial

free trial version available

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++

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
Windows

hosted

AIX
Linux
Windows

Data scheme

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

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

.Net
ODBC
RESTful HTTP API

JDBC
ODBC
Proprietary protocol

Supported programming languages

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

Go
JavaScript (Node.js)
Python

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

Lua

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

depending on storage layer

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

yes

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

managed by 'Learn by Usage'

User concepts

Access control

Administrators, Users, Groups

Access rights for users, groups and roles according to SQL-standard

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More resources

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