DBMS > Google Cloud Datastore vs. OrientDB vs. Sadas Engine vs. WakandaDB

System Properties Comparison Google Cloud Datastore vs. OrientDB vs. Sadas Engine vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

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

Multi-model DBMS (Document, Graph, Key/Value)

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Document store

Document store
Graph DBMS
Key-value store

Relational DBMS

Object oriented DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

cloud.google.com/datastore

orientdb.org

www.sadasengine.com

wakanda.github.io

Technical documentation

cloud.google.com/datastore/docs

www.orientdb.com/docs/last/index.html

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

wakanda.github.io/doc

Developer

Google

OrientDB LTD; CallidusCloud; SAP

SADAS s.r.l.

Wakanda SAS

Initial release

2008

2010

2006

2012

Current release

3.2.29, March 2024

8.0

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

free trial version available

Open Source

AGPLv3, extended commercial license available

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

Java

C++

C++, JavaScript

Server operating systems

hosted

All OS with a Java JDK (>= JDK 6)

AIX
Linux
Windows

Linux
OS X
Windows

Data scheme

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

Typing

predefined data types such as float or date

yes, details here

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language, no joins

yes

APIs and other access methods

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

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

JDBC
ODBC
Proprietary protocol

RESTful HTTP API

Supported programming languages

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

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

JavaScript

Server-side scripts

Stored procedures

using Google App Engine

Java, Javascript

yes

Triggers

Callbacks using the Google Apps Engine

Hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

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

relationship in graphs

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

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

managed by 'Learn by Usage'

User concepts

Access control

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

Access rights for users and roles; record level security configurable

Access rights for users, groups and roles according to SQL-standard

yes

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More resources

DB-Engines blog posts

Graph DBMS increased their popularity by 500% within the last 2 years
3 March 2015, Paul Andlinger

Graph DBMSs are gaining in popularity faster than any other database category
21 January 2014, Matthias Gelbmann

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