DBMS > Amazon DynamoDB vs. LevelDB vs. Microsoft Azure SQL Database vs. RRDtool vs. Teradata Aster

System Properties Comparison Amazon DynamoDB vs. LevelDB vs. Microsoft Azure SQL Database vs. RRDtool vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Database as a Service offering with high compatibility to Microsoft SQL Server

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Platform for big data analytics on multistructured data sources and types

Primary database model

Document store
Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	1.90
Rank	#132	Overall
	#11	Time Series DBMS

Website

aws.amazon.com/dynamodb

github.com/google/leveldb

azure.microsoft.com/en-us/products/azure-sql/database

oss.oetiker.ch/rrdtool

Technical documentation

docs.aws.amazon.com/dynamodb

github.com/google/leveldb/blob/main/doc/index.md

docs.microsoft.com/en-us/azure/azure-sql

oss.oetiker.ch/rrdtool/doc

Developer

Amazon

Google

Microsoft

Tobias Oetiker

Teradata

Initial release

2012

2011

2010

1999

2005

Current release

1.23, February 2021

V12

1.8.0, 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

BSD

commercial

Open Source

GPL V2 and FLOSS

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++

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

hosted

Illumos
Linux
OS X
Windows

hosted

HP-UX
Linux

Linux

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

Numeric data only

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

Exporting into and restoring from XML files possible

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC

in-process shared library
Pipes

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

Transact SQL

R packages

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes, with always 3 replicas available

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

none

Immediate Consistency or Eventual Consistency depending on configuration

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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

fine grained access rights according to SQL-standard

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