DBMS > Amazon Aurora vs. Apache Impala vs. atoti vs. Galaxybase

System Properties Comparison Amazon Aurora vs. Apache Impala vs. atoti vs. Galaxybase

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Analytic DBMS for Hadoop

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Scalable, ACID-compliant native distributed parallel graph platform

Primary database model

Relational DBMS

Object oriented DBMS

Graph DBMS

Secondary database models

Document store

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	0.00
Rank	#383	Overall
	#39	Graph DBMS

Website

aws.amazon.com/rds/aurora

impala.apache.org

atoti.io

galaxybase.com

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

impala.apache.org/impala-docs.html

docs.atoti.io

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

ActiveViam

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

Initial release

2015

2013

2017

Current release

4.1.0, June 2022

Nov 20, November 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

free versions available

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++

Java

C and Java

Server operating systems

hosted

Linux

Data scheme

yes

Strong typed schema

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

Multidimensional Expressions (MDX)

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC

Go
Java
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Python

user defined procedures and functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

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

Relationships in graphs

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Role-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

Amazon - the rising star in the DBMS market
3 August 2015, Matthias Gelbmann

show all

Recent citations in the news

Continuously replicate Amazon DynamoDB changes to Amazon Aurora PostgreSQL using AWS Lambda | Amazon ...
14 May 2024, AWS Blog

Join the preview of Amazon Aurora Limitless Database | Amazon Web Services
27 November 2023, AWS Blog

New – Amazon Aurora Optimized Reads for Aurora PostgreSQL with up to 8x query latency improvement for I/O ...
8 November 2023, AWS Blog

Build generative AI applications with Amazon Aurora and Knowledge Bases for Amazon Bedrock | Amazon Web Services
2 February 2024, AWS Blog

Improve the performance of generative AI workloads on Amazon Aurora with Optimized Reads and pgvector | Amazon ...
9 February 2024, AWS Blog

provided by Google News

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

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

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

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

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

provided by Google News

FRTB product of the year: ActiveViam
28 November 2023, Risk.net

provided by Google News

Share this page