DBMS > Google Cloud Datastore vs. Oracle NoSQL vs. Sadas Engine vs. TigerGraph

System Properties Comparison Google Cloud Datastore vs. Oracle NoSQL vs. Sadas Engine vs. TigerGraph

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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

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

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

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Document store

Document store
Key-value store
Relational DBMS

Relational DBMS

Graph DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

cloud.google.com/datastore

www.oracle.com/database/nosql/technologies/nosql

www.sadasengine.com

www.tigergraph.com

Technical documentation

cloud.google.com/datastore/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

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

docs.tigergraph.com

Developer

Google

Oracle

SADAS s.r.l.

Initial release

2008

2011

2006

2017

Current release

23.3, December 2023

8.0

License

Commercial or Open Source

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

commercial

free trial version 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

Java

C++

Server operating systems

hosted

Linux
Solaris SPARC/x86

AIX
Linux
Windows

Linux

Data scheme

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

yes, details here

optional

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 DML and DDL statements

yes

SQL-like query language (GSQL)

APIs and other access methods

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

RESTful HTTP API

JDBC
ODBC
Proprietary protocol

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

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

C
C#
Go
Java
JavaScript (Node.js)
Python

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

C++
Java

Server-side scripts

Stored procedures

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

yes

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.

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Relationships in graphs

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

configurable

ACID within a storage node (=shard)

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

off heap cache

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

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

Role-based access control

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