DBMS > Apache Kylin vs. Google BigQuery vs. Percona Server for MongoDB vs. SWC-DB

System Properties Comparison Apache Kylin vs. Google BigQuery vs. Percona Server for MongoDB vs. SWC-DB

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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

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Document store

Wide column store

Secondary database models

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	0.60
Rank	#246	Overall
	#39	Document stores

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

kylin.apache.org

cloud.google.com/bigquery

www.percona.com/mongodb/software/percona-server-for-mongodb

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

Technical documentation

kylin.apache.org/docs

cloud.google.com/bigquery/docs

docs.percona.com/percona-distribution-for-mongodb

Developer

Apache Software Foundation, originally contributed from eBay Inc

Google

Percona

Alex Kashirin

Initial release

2015

2010

2015

2020

Current release

3.1.0, July 2020

3.4.10-2.10, November 2017

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL Version 2

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux

hosted

Linux

Data scheme

yes

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

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

proprietary protocol using JSON

Proprietary protocol
Thrift

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

C++

Server-side scripts

Stored procedures

user defined functions

in JavaScript

JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

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

via In-Memory Engine

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 and roles

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