DBMS > FeatureBase vs. Microsoft Azure Table Storage vs. OushuDB vs. SAP HANA

System Properties Comparison FeatureBase vs. Microsoft Azure Table Storage vs. OushuDB vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

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

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.23
Rank	#296	Overall
	#134	Relational DBMS

Score	3.55
Rank	#80	Overall
	#6	Wide column stores

Score	0.05
Rank	#357	Overall
	#151	Relational DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Website

www.featurebase.com

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

www.oushu.com/product/oushuDB

www.sap.com/products/hana.html

Technical documentation

docs.featurebase.com

www.oushu.com/documentation

help.sap.com/hana

Developer

Molecula and Pilosa Open Source Contributors

Microsoft

Oushu

SAP

Initial release

2017

2012

2010

Current release

2022, May 2022

4.0.1, August 2020

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Server operating systems

Linux
macOS

hosted

Linux

Appliance or cloud-service

Data scheme

yes

schema-free

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 queries

Full-featured ANSI SQL support

yes

APIs and other access methods

gRPC
JDBC
Kafka Connector
ODBC

RESTful HTTP API

JDBC
ODBC

Supported programming languages

Java
Python

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

C
C++

Server-side scripts

Stored procedures

yes

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes

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.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, using Linux fsync

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 based on private key authentication or shared access signatures

Kerberos, SSL and role based access

yes

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