DBMS > Amazon Neptune vs. Apache Kylin vs. Google BigQuery vs. Sphinx vs. YDB

System Properties Comparison Amazon Neptune vs. Apache Kylin vs. Google BigQuery vs. Sphinx vs. YDB

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

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

Open source search engine for searching in data from different sources, e.g. relational databases

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Graph DBMS
RDF store

Relational DBMS

Search engine

Document store
Relational DBMS

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

Score	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.33
Rank	#287	Overall
	#43	Document stores
	#132	Relational DBMS

Website

aws.amazon.com/neptune

kylin.apache.org

cloud.google.com/bigquery

sphinxsearch.com

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

aws.amazon.com/neptune/developer-resources

kylin.apache.org/docs

cloud.google.com/bigquery/docs

sphinxsearch.com/docs

ydb.tech/en/docs

Developer

Amazon

Apache Software Foundation, originally contributed from eBay Inc

Google

Sphinx Technologies Inc.

Yandex

Initial release

2017

2015

2010

2001

2019

Current release

3.1.0, July 2020

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2, commercial licence available

Open Source

Apache 2.0; 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++

Server operating systems

hosted

Linux

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, 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

full-text index on all search fields

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

SQL-like query language (SphinxQL)

SQL-like query language (YQL)

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

Proprietary protocol

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

none

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

The original contents of fields are not stored in the Sphinx index.

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 rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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

Google Cloud Identity & Access Management (IAM)

Access rights defined for Yandex Cloud users

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