DBMS > BigObject vs. Hive vs. Microsoft Azure SQL Database vs. ReductStore

System Properties Comparison BigObject vs. Hive vs. Microsoft Azure SQL Database vs. ReductStore

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Database as a Service offering with high compatibility to Microsoft SQL Server

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Website

bigobject.io

hive.apache.org

azure.microsoft.com/en-us/products/azure-sql/database

github.com/reductstore
www.reduct.store

Technical documentation

docs.bigobject.io

cwiki.apache.org/confluence/display/Hive/Home

docs.microsoft.com/en-us/azure/azure-sql

www.reduct.store/docs

Developer

BigObject, Inc.

Apache Software Foundation

initially developed by Facebook

Microsoft

ReductStore LLC

Initial release

2015

2012

2010

2023

Current release

3.1.3, April 2022

V12

1.9, March 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2

commercial

Open Source

Business Source License 1.1

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

Java

C++

C++, Rust

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All OS with a Java VM

hosted

Docker
Linux
macOS
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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC

HTTP API

Supported programming languages

C++
Java
PHP
Python

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

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

Lua

yes

user defined functions and integration of map-reduce

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

yes, with always 3 replicas available

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

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

ACID

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

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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More resources

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