DBMS > Google Cloud Datastore vs. Neo4j vs. SAP HANA vs. SWC-DB

System Properties Comparison Google Cloud Datastore vs. Neo4j vs. SAP HANA vs. SWC-DB

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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

Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings

In-memory, column based data store. Available as appliance or cloud service

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Time Series DBMS

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	44.89
Rank	#21	Overall
	#1	Graph DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

cloud.google.com/datastore

neo4j.com

www.sap.com/products/hana.html

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

cloud.google.com/datastore/docs

neo4j.com/docs

help.sap.com/hana

Developer

Google

Neo4j, Inc.

SAP

Alex Kashirin

Initial release

2008

2007

2010

2020

Current release

5.20, May 2024

2.0 SPS07 (April 4, 2023), April 2023

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

GPL version3, commercial licenses available

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.

Implementation language

Java, Scala

C++

Server operating systems

hosted

Linux

Can also be used server-less as embedded Java database.
OS X
Solaris
Windows

Appliance or cloud-service

Linux

Data scheme

schema-free

schema-free and schema-optional

yes

schema-free

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

pluggable indexing subsystem, by default Apache Lucene

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL-like query language

APIs and other access methods

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

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

JDBC
ODBC

Proprietary protocol
Thrift

Supported programming languages

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

.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala

C++

Server-side scripts

Stored procedures

using Google App Engine

yes

User defined Procedures and Functions

SQLScript, R

Triggers

Callbacks using the Google Apps Engine

yes

via event handler

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes using Neo4j Fabric

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Causal Clustering using Raft protocol

available in in Enterprise Version only

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

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.

Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Relationships 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

User concepts

Access control

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

Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

yes

More information provided by the system vendor

Specific characteristics

Neo4j delivers graph technology that has been battle tested for performance and scale...
» more

Competitive advantages

Neo4j is the market leader, graph database category creator, and the most widely...
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Typical application scenarios

Real-Time Recommendations Master Data Management Identity and Access Management Network...
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Key customers

Over 800 commercial customers and over 4300 startups use Neo4j. Flagship customers...
» more

Market metrics

Neo4j boasts the world's largest graph database ecosystem with more than 140 million...
» more

Licensing and pricing models

GPL v3 license that can be used all the places where you might use MySQL. Neo4j Commercial...
» more

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