DBMS > HBase vs. Microsoft SQL Server vs. OpenEdge vs. Spark SQL vs. Trafodion

System Properties Comparison HBase vs. Microsoft SQL Server vs. OpenEdge vs. Spark SQL vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

Microsofts flagship relational DBMS

Application development environment with integrated database management system

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

hbase.apache.org

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

www.progress.com/openedge

spark.apache.org/sql

trafodion.apache.org

Technical documentation

hbase.apache.org/book.html

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

documentation.progress.com/output/ua/OpenEdge_latest

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

trafodion.apache.org/documentation.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

Progress Software Corporation

Apache Software Foundation

Apache Software Foundation, originally developed by HP

Initial release

2008

1989

1984

2014

Current release

2.3.4, January 2021

SQL Server 2022, November 2022

OpenEdge 12.2, March 2020

3.5.0 ( 2.13), September 2023

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache version 2

commercial

restricted free version is available

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Scala

C++, Java

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Linux

Data scheme

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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

yes

close to SQL 92

SQL-like DML and DDL statements

yes

APIs and other access methods

Java API
RESTful HTTP API
Thrift

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

JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

Progress proprietary ABL (Advanced Business Language)

Java
Python
R
Scala

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

Coprocessors in Java

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

yes

Java Stored Procedures

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

horizontal partitioning

since Version 11.4

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

Source-replica replication

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

Users and groups

fine grained access rights according to SQL-standard

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