DBMS > Apache Kylin vs. Google BigQuery vs. Neo4j vs. Prometheus

System Properties Comparison Apache Kylin vs. Google BigQuery vs. Neo4j vs. Prometheus

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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

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

Open-source Time Series DBMS and monitoring system

Primary database model

Relational DBMS

Graph DBMS

Time Series DBMS

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	44.89
Rank	#21	Overall
	#1	Graph DBMS

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Website

kylin.apache.org

cloud.google.com/bigquery

neo4j.com

prometheus.io

Technical documentation

kylin.apache.org/docs

cloud.google.com/bigquery/docs

neo4j.com/docs

prometheus.io/docs

Developer

Apache Software Foundation, originally contributed from eBay Inc

Google

Neo4j, Inc.

Initial release

2015

2010

2007

2015

Current release

3.1.0, July 2020

5.20, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version3, commercial licenses available

Open Source

Apache 2.0

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

Server operating systems

Linux

hosted

Linux

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

Linux
Windows

Data scheme

yes

schema-free and schema-optional

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

pluggable indexing subsystem, by default Apache Lucene

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP 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

RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

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

yes using Neo4j Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Causal Clustering using Raft protocol

available in in Enterprise Version only

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

none

Foreign keys

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

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)

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...
» 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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