DBMS > Apache Kylin vs. Transwarp StellarDB vs. YottaDB

System Properties Comparison Apache Kylin vs. Transwarp StellarDB vs. YottaDB

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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

A distributed graph DBMS built for enterprise-level graph applications

A fast and solid embedded Key-value store

Primary database model

Relational DBMS

Graph DBMS

Key-value store

Secondary database models

Relational DBMS

using the Octo plugin

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	0.07
Rank	#371	Overall
	#39	Graph DBMS

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

kylin.apache.org

www.transwarp.cn/en/product/stellardb

yottadb.com

Technical documentation

kylin.apache.org/docs

yottadb.com/resources/documentation

Developer

Apache Software Foundation, originally contributed from eBay Inc

Transwarp

YottaDB, LLC

Initial release

2015

2001

Current release

3.1.0, July 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

AGPL 3.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux

Docker
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)

SQL-like query language

by using the Octo plugin

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

OpenCypher

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Transaction concepts

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

optimistic locking

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

yes

Users and groups based on OS-security mechanisms

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More resources

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provided by Google News

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