DBMS > Apache Kylin vs. Google BigQuery vs. Google Cloud Spanner vs. Neo4j vs. SWC-DB

System Properties Comparison Apache Kylin vs. Google BigQuery vs. Google Cloud Spanner vs. Neo4j vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Large scale data warehouse service with append-only tables

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

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

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Graph DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	44.46
Rank	#23	Overall
	#1	Graph DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

kylin.apache.org

cloud.google.com/bigquery

cloud.google.com/spanner

neo4j.com

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

Technical documentation

kylin.apache.org/docs

cloud.google.com/bigquery/docs

cloud.google.com/spanner/docs

neo4j.com/docs

Developer

Apache Software Foundation, originally contributed from eBay Inc

Google

Neo4j, Inc.

Alex Kashirin

Initial release

2015

2010

2017

2007

2020

Current release

3.1.0, July 2020

5.19, April 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version3, commercial licenses available

Open Source

GPL V3

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.

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

Implementation language

Java

Java, Scala

C++

Server operating systems

Linux

hosted

Linux

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

Linux

Data scheme

yes

schema-free and schema-optional

schema-free

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.

Secondary indexes

yes

pluggable indexing subsystem, by default Apache Lucene

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

Query statements complying to ANSI 2011

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

Proprietary protocol
Thrift

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

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

C++

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

User defined Procedures and Functions

Triggers

yes

via event handler

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes using Neo4j Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

Causal Clustering using Raft protocol

available in in Enterprise Version only

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

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

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Relationships in graphs

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Strict serializable isolation

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.

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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)

More information provided by the system vendor

Specific characteristics

Neo4j delivers graph technology that has been battle tested for performance and scale...
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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...
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Market metrics

Neo4j boasts the world's largest graph database ecosystem with more than 140 million...
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Licensing and pricing models

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