DBMS > Apache Impala vs. Hive vs. Netezza vs. Oracle vs. OrientDB

System Properties Comparison Apache Impala vs. Hive vs. Netezza vs. Oracle vs. OrientDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Data warehouse and analytics appliance part of IBM PureSystems

Widely used RDBMS

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store

Secondary database models

Document store

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Website

impala.apache.org

hive.apache.org

www.ibm.com/products/netezza

www.oracle.com/database

orientdb.org

Technical documentation

impala.apache.org/impala-docs.html

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

docs.oracle.com/en/database

www.orientdb.com/docs/last/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

initially developed by Facebook

IBM

Oracle

OrientDB LTD; CallidusCloud; SAP

Initial release

2013

2012

2000

1980

2010

Current release

4.1.0, June 2022

3.1.3, April 2022

23c, September 2023

3.2.29, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C and C++

Java

Server operating systems

Linux

All OS with a Java VM

Linux

included in appliance

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

Schemaless in JSON and XML columns

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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

with proprietary extensions

SQL-like query language, no joins

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift

JDBC
ODBC
OLE DB

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
PHP
Python

C
C++
Fortran
Java
Lua
Perl
Python
R

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

user defined functions and integration of map-reduce

yes

PL/SQL

also stored procedures in Java possible

Java, Javascript

Triggers

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

Multi-source replication
Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

query execution via MapReduce

yes

can be realized in PL/SQL

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

ACID

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users, groups and roles

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

Access rights for users and roles; record level security configurable

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