DBMS > Amazon DynamoDB vs. Google Cloud Datastore vs. Oracle Berkeley DB vs. SAP IQ vs. SQream DB

System Properties Comparison Amazon DynamoDB vs. Google Cloud Datastore vs. Oracle Berkeley DB vs. SAP IQ vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Widely used in-process key-value store

Columnar RDBMS optimized for Big Data analytics

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Document store
Key-value store

Document store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

column-oriented

Relational 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	4.36
Rank	#72	Overall
	#12	Document stores

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	2.64
Rank	#108	Overall
	#52	Relational DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

aws.amazon.com/dynamodb

cloud.google.com/datastore

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

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

sqream.com

Technical documentation

docs.aws.amazon.com/dynamodb

cloud.google.com/datastore/docs

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

help.sap.com/docs/SAP_IQ

docs.sqream.com

Developer

Amazon

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SAP, formerly Sybase

SQream Technologies

Initial release

2012

2008

1994

2017

Current release

18.1.40, May 2020

16.1 SPS04, April 2019

2022.1.6, December 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

commercial license available

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

C++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

yes, ANSI Standard SQL Types

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

SQL-like query language (GQL)

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
RESTful HTTP API

.Net
JDBC
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.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

C
C#
C++
Java
Perl
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

using Google App Engine

yes

user defined functions in Python

Triggers

yes

by integration with AWS Lambda

Callbacks using the Google Apps Engine

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

shared disk or shared nothing architectures with SAP IQ Multiplexer

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

Source-replica replication

SAP/Sybase Replication Server

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

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

Serializable Isolation within Transactions, Read Committed outside of Transactions

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, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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