DBMS > Apache Impala vs. EJDB vs. Hive vs. Netezza

System Properties Comparison Apache Impala vs. EJDB vs. Hive vs. Netezza

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

Data warehouse and analytics appliance part of IBM PureSystems

Primary database model

Relational DBMS

Document store

Relational DBMS

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Website

impala.apache.org

github.com/Softmotions/ejdb

hive.apache.org

www.ibm.com/products/netezza

Technical documentation

impala.apache.org/impala-docs.html

github.com/Softmotions/ejdb/blob/master/README.md

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Softmotions

Apache Software Foundation

initially developed by Facebook

IBM

Initial release

2013

2012

2000

Current release

4.1.0, June 2022

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GPLv2

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java

Server operating systems

Linux

server-less

All OS with a Java VM

Linux

included in appliance

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

in-process shared library

JDBC
ODBC
Thrift

JDBC
ODBC
OLE DB

Supported programming languages

All languages supporting JDBC/ODBC

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C++
Java
PHP
Python

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

user defined functions and integration of map-reduce

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

Access rights for users, groups and roles

Users with fine-grained authorization concept

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More resources

DB-Engines blog posts

Why is Hadoop not listed in the DB-Engines Ranking?
13 May 2013, Paul Andlinger

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