DBMS > DolphinDB vs. FatDB vs. Infobright vs. Spark SQL vs. Teradata Aster

System Properties Comparison DolphinDB vs. FatDB vs. Infobright vs. Spark SQL vs. Teradata Aster

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

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.

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Spark SQL is a component on top of 'Spark Core' for structured data processing

Platform for big data analytics on multistructured data sources and types

Primary database model

Time Series DBMS

Document store
Key-value store

Relational DBMS

Secondary database models

Relational DBMS

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.dolphindb.com

ignitetech.com/softwarelibrary/infobrightdb

spark.apache.org/sql

Technical documentation

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

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

DolphinDB, Inc

FatCloud

Ignite Technologies Inc.; formerly InfoBright Inc.

Apache Software Foundation

Teradata

Initial release

2018

2012

2005

2014

2005

Current release

v2.00.4, January 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free community version available

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Scala

Server operating systems

Linux
Windows

Windows

Linux
Windows

Linux
OS X
Windows

Linux

Data scheme

yes

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

in Aster File Store

Secondary indexes

yes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

SQL-like query language

Via inetgration in SQL Server

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

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

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

ADO.NET
JDBC
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

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

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Python
R
Scala

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

via applications

R packages

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Source-replica replication

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

Transaction concepts

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

yes

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

Can implement custom security layer via applications

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

fine grained access rights according to SQL-standard

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