DBMS > Apache Kylin vs. EventStoreDB vs. Netezza vs. Snowflake vs. TerarkDB

System Properties Comparison Apache Kylin vs. EventStoreDB vs. Netezza vs. Snowflake vs. TerarkDB

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

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Data warehouse and analytics appliance part of IBM PureSystems

Cloud-based data warehousing service for structured and semi-structured data

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

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

Score	1.10
Rank	#170	Overall
	#77	Relational DBMS

Score	1.07
Rank	#175	Overall
	#1	Event Stores

Score	6.70
Rank	#52	Overall
	#31	Relational DBMS

Score	142.50
Rank	#7	Overall
	#5	Relational DBMS

Score	0.00
Rank	#386	Overall
	#61	Key-value stores

Website

kylin.apache.org

www.eventstore.com

www.ibm.com/products/netezza

www.snowflake.com

github.com/bytedance/terarkdb

Technical documentation

kylin.apache.org/docs

developers.eventstore.com

docs.snowflake.net/manuals/index.html

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Apache Software Foundation, originally contributed from eBay Inc

Event Store Limited

IBM

Snowflake Computing Inc.

ByteDance, originally Terark

Initial release

2015

2012

2000

2014

2016

Current release

3.1.0, July 2020

21.2, February 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux

Linux
Windows

Linux

included in appliance

hosted

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
OLE DB

CLI Client
JDBC
ODBC

C++ API
Java API

Supported programming languages

C
C++
Fortran
Java
Lua
Perl
Python
R

JavaScript (Node.js)
Python

C++
Java

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

none

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

Transaction concepts

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

yes

User concepts

Access control

Users with fine-grained authorization concept

Users with fine-grained authorization concept, user roles and pluggable authentication

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