DBMS > Amazon DynamoDB vs. AnzoGraph DB vs. Microsoft SQL Server vs. Oracle Berkeley DB

System Properties Comparison Amazon DynamoDB vs. AnzoGraph DB vs. Microsoft SQL Server vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

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

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Microsofts flagship relational DBMS

Widely used in-process key-value store

Primary database model

Document store
Key-value store

Graph DBMS
RDF store

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

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

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Website

aws.amazon.com/dynamodb

cambridgesemantics.com/anzograph

www.microsoft.com/en-us/sql-server

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

docs.aws.amazon.com/dynamodb

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

learn.microsoft.com/en-US/sql/sql-server

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Amazon

Cambridge Semantics

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2012

2018

1989

1994

Current release

2.3, January 2021

SQL Server 2022, November 2022

18.1.40, May 2020

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free trial version available

commercial

restricted free version is available

Open Source

commercial license available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

hosted

Linux

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

schema-free

Schema-free and OWL/RDFS-schema support

yes

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

SQL interfaced based on SQLite is available

APIs and other access methods

RESTful HTTP API

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C++
Java
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

by integration with AWS Lambda

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

yes, but depending on the SQL-Server Edition

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

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

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

Access rights for users and roles

fine grained access rights according to SQL-standard

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