DBMS > Apache Impala vs. Microsoft Azure SQL Database vs. SAP Adaptive Server

System Properties Comparison Apache Impala vs. Microsoft Azure SQL Database vs. SAP Adaptive Server

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

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

Primary database model

Relational DBMS

Secondary database models

Document store

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	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	72.95
Rank	#16	Overall
	#11	Relational DBMS

Score	32.67
Rank	#25	Overall
	#17	Relational DBMS

Website

impala.apache.org

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

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

Technical documentation

impala.apache.org/impala-docs.html

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

help.sap.com/docs/SAP_ASE

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Microsoft

SAP, Sybase

Initial release

2013

2010

1987

Current release

4.1.0, June 2022

V12

16.0

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

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

C++

C and C++

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
Solaris
Unix
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

JDBC
ODBC

ADO.NET
JDBC
ODBC

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

Supported programming languages

All languages supporting JDBC/ODBC

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

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Transact SQL

Java and Transact-SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes, with always 3 replicas available

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

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.

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

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Recent citations in the news

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

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29 July 2021, iProgrammer

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30 June 2022, SiliconANGLE News

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