DBMS > BigObject vs. Sequoiadb vs. Spark SQL vs. Valentina Server

System Properties Comparison BigObject vs. Sequoiadb vs. Spark SQL vs. Valentina Server

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

NewSQL database with distributed OLTP and SQL

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

Object-relational database and reports server

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Relational DBMS

Relational DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

Technical documentation

docs.bigobject.io

www.sequoiadb.com/en/index.php?m=Files&a=index

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

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

BigObject, Inc.

Sequoiadb Ltd.

Apache Software Foundation

Paradigma Software

Initial release

2015

2013

2014

1999

Current release

3.5.0 ( 2.13), September 2023

5.7.5

License

Commercial or Open Source

commercial

free community edition available

Open Source

Server: AGPL; Client: Apache V2

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

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

proprietary protocol using JSON

JDBC
ODBC

ODBC

Supported programming languages

.Net
C++
Java
PHP
Python

Java
Python
R
Scala

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

Lua

JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual 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

Document is locked during a transaction

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

User concepts

Access control

simple password-based access control

fine grained access rights according to SQL-standard

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More resources

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