DBMS > Apache Impala vs. Microsoft SQL Server vs. MonetDB vs. SAP HANA vs. TerminusDB

System Properties Comparison Apache Impala vs. Microsoft SQL Server vs. MonetDB vs. SAP HANA vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Microsofts flagship relational DBMS

A relational database management system that stores data in columns

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Relational DBMS

Graph DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Document store
RDF store

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

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	1.72
Rank	#148	Overall
	#68	Relational DBMS

Score	45.84
Rank	#22	Overall
	#16	Relational DBMS

Score	0.22
Rank	#323	Overall
	#28	Graph DBMS

Website

impala.apache.org

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

www.monetdb.org

www.sap.com/products/hana.html

terminusdb.com

Technical documentation

impala.apache.org/impala-docs.html

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

www.monetdb.org/Documentation

help.sap.com/hana

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Microsoft

MonetDB BV

SAP

DataChemist Ltd.

Initial release

2013

1989

2004

2010

2018

Current release

4.1.0, June 2022

SQL Server 2022, November 2022

Dec2023 (11.49), December 2023

2.0 SPS07 (April 4, 2023), April 2023

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

Mozilla Public License 2.0

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Prolog, Rust

Server operating systems

Linux

Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Appliance or cloud-service

Linux

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

SQL 2003 with some extensions

yes

SQL-like query language (WOQL)

APIs and other access methods

JDBC
ODBC

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

All languages supporting JDBC/ODBC

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

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

JavaScript
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

yes, in SQL, C, R

SQLScript, R

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding via remote tables

yes

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes, but depending on the SQL-Server Edition

none

Source-replica replication available in experimental status

yes

Journaling Streams

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

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

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

yes

Role-based access control

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