DBMS > Amazon SimpleDB vs. AnzoGraph DB vs. Snowflake vs. Spark SQL

System Properties Comparison Amazon SimpleDB vs. AnzoGraph DB vs. Snowflake vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

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

Cloud-based data warehousing service for structured and semi-structured data

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Key-value store

Graph DBMS
RDF store

Relational DBMS

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

Score	1.88
Rank	#133	Overall
	#23	Key-value stores

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

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/simpledb

cambridgesemantics.com/anzograph

www.snowflake.com

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/simpledb

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

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Amazon

Cambridge Semantics

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2007

2018

2014

Current release

2.3, January 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

Apache 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

Scala

Server operating systems

hosted

Linux

hosted

Linux
OS X
Windows

Data scheme

schema-free

Schema-free and OWL/RDFS-schema support

yes

support of semi-structured data formats (JSON, XML, Avro)

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

All columns are indexed automatically

SQL

Support of SQL

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

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

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

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

C++
Java
Python

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Automatic sharding

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Concurrent data updates can be detected by the application

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

Users with fine-grained authorization concept, user roles and pluggable authentication

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