DBMS > Apache Impala vs. EsgynDB vs. H2 vs. Netezza

System Properties Comparison Apache Impala vs. EsgynDB vs. H2 vs. Netezza

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Data warehouse and analytics appliance part of IBM PureSystems

Primary database model

Relational DBMS

Secondary database models

Document store

Spatial DBMS

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	7.57
Rank	#47	Overall
	#30	Relational DBMS

Score	7.56
Rank	#48	Overall
	#31	Relational DBMS

Website

impala.apache.org

www.esgyn.cn

www.h2database.com

www.ibm.com/products/netezza

Technical documentation

impala.apache.org/impala-docs.html

www.h2database.com/html/main.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Esgyn

Thomas Mueller

IBM

Initial release

2013

2015

2005

2000

Current release

4.1.0, June 2022

2.2.220, July 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

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++

C++, Java

Java

Server operating systems

Linux

All OS with a Java VM

Linux

included in appliance

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.

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

JDBC
ODBC

JDBC
ODBC
OLE DB

Supported programming languages

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/ADO.Net

Java

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

Java Stored Procedures and User-Defined Functions

yes

Triggers

yes

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

Multi-source replication between multi datacenters

With clustering: 2 database servers on different computers operate on identical copies of a database

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

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

yes, multi-version concurrency control (MVCC)

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 rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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More resources

Recent citations in the news

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

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

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

Unify and share data across Netezza and watsonx.data for new generative AI applications
21 June 2024, ibm.com

How to migrate a large data warehouse from IBM Netezza to Amazon Redshift with no downtime
21 August 2019, AWS Blog

AWS and IBM Netezza come out in support of Iceberg in table format face-off
1 August 2023, The Register

Copy data from Netezza to Azure with Azure Data Factory
9 September 2019, Microsoft

IBM Completes Acquisition of Netezza
11 November 2010, PR Newswire

provided by Google News

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