DBMS > Apache Derby vs. Apache Impala vs. DuckDB

System Properties Comparison Apache Derby vs. Apache Impala vs. DuckDB

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Editorial information provided by DB-Engines

Name

Description

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

Analytic DBMS for Hadoop

An embeddable, in-process, column-oriented SQL OLAP RDBMS

Primary database model

Relational DBMS

Secondary database models

Document store

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

Score	3.04
Rank	#85	Overall
	#46	Relational DBMS

Score	11.68
Rank	#41	Overall
	#25	Relational DBMS

Score	7.44
Rank	#44	Overall
	#27	Relational DBMS

Website

db.apache.org/derby

impala.apache.org

duckdb.org

Technical documentation

db.apache.org/derby/manuals/index.html

impala.apache.org/impala-docs.html

duckdb.org/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Initial release

1997

2013

2018

Current release

10.17.1.0, November 2023

4.1.0, June 2022

1.0.0, June 2024

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM

Linux

server-less

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

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC

JDBC
ODBC

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

Supported programming languages

Java

All languages supporting JDBC/ODBC

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

Server-side scripts

Stored procedures

Java Stored Procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

none

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

Immediate Consistency

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)

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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Recent citations in the news

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3 March 2025, CybersecurityNews

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30 June 2022, SiliconANGLE

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24 June 2022, InfoWorld

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20 June 2022, datanami.com

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