DBMS > Amazon Aurora vs. Amazon DynamoDB vs. Apache Impala vs. Apache IoTDB

System Properties Comparison Amazon Aurora vs. Amazon DynamoDB vs. Apache Impala vs. Apache IoTDB

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Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Analytic DBMS for Hadoop

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Primary database model

Relational DBMS

Document store
Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Document store

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

Score	7.84
Rank	#44	Overall
	#28	Relational DBMS

Score	70.06
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Website

aws.amazon.com/rds/aurora

aws.amazon.com/dynamodb

impala.apache.org

iotdb.apache.org

Technical documentation

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

docs.aws.amazon.com/dynamodb

impala.apache.org/impala-docs.html

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

Initial release

2015

2012

2013

2018

Current release

4.1.0, June 2022

1.1.0, April 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2

Open Source

Apache Version 2.0

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

Server operating systems

hosted

Linux

All OS with a Java VM (>= 1.8)

Data scheme

yes

schema-free

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

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP API

JDBC
ODBC

JDBC
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

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

selectable replication factor

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

query execution via MapReduce

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Eventual Consistency
Strong Consistency with Raft

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

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