DBMS > Hive vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. Oracle Berkeley DB vs. SAP Adaptive Server

System Properties Comparison Hive vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. Oracle Berkeley DB vs. SAP Adaptive Server

Editorial information provided by DB-Engines

Name

Description

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

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

Microsofts flagship relational DBMS

Widely used in-process key-value store

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Primary database model

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

Website

hive.apache.org

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

www.microsoft.com/en-us/sql-server

www.oracle.com/database/technologies/related/berkeleydb.html

www.sap.com/products/technology-platform/sybase-ase.html

Technical documentation

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

learn.microsoft.com/en-US/sql/sql-server

docs.oracle.com/cd/E17076_05/html/index.html

help.sap.com/docs/SAP_ASE

Developer

Apache Software Foundation

initially developed by Facebook

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SAP, Sybase

Initial release

2012

1989

1994

1987

Current release

3.1.3, April 2022

SQL Server 2022, November 2022

18.1.40, May 2020

16.0

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

C, Java, C++ (depending on the Berkeley DB edition)

C and C++

Server operating systems

All OS with a Java VM

hosted

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

Data scheme

yes

schema-free

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

JDBC
ODBC
Thrift

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C++
Java
PHP
Python

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Java and Transact-SQL

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, but depending on the SQL-Server Edition

Source-replica replication

Multi-source replication
Source-replica replication

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking

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

Access rights for users, groups and roles

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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