DBMS > Amazon CloudSearch vs. AnzoGraph DB vs. Microsoft Azure Table Storage vs. SQLite vs. Teradata

System Properties Comparison Amazon CloudSearch vs. AnzoGraph DB vs. Microsoft Azure Table Storage vs. SQLite vs. Teradata

Editorial information provided by DB-Engines

Name

Description

A hosted search engine 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

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used embeddable, in-process RDBMS

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.81
Rank	#137	Overall
	#12	Search engines

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

aws.amazon.com/cloudsearch

cambridgesemantics.com/anzograph

azure.microsoft.com/en-us/services/storage/tables

www.sqlite.org

www.teradata.com

Technical documentation

docs.aws.amazon.com/cloudsearch

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

www.sqlite.org/docs.html

docs.teradata.com

Developer

Amazon

Cambridge Semantics

Microsoft

Dwayne Richard Hipp

Teradata

Initial release

2012

2018

2012

2000

1984

Current release

2.3, January 2021

3.46.0 (23 May 2024), May 2024

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

Public Domain

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

Server operating systems

hosted

Linux

hosted

server-less

hosted
Linux

Data scheme

yes

Schema-free and OWL/RDFS-schema support

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

all search fields are automatically indexed

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

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

yes

SQL-92 is not fully supported

yes

SQL 2016 + extensions

APIs and other access methods

HTTP API

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

RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

C++
Java
Python

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

Automatic sharding

Sharding

Implicit feature of the cloud service

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

Multi-source replication in MPP-Cluster

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

authentication via encrypted signatures

Access rights for users and roles

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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.