DBMS > Apache Kylin vs. Heroic vs. Microsoft Azure Synapse Analytics vs. Snowflake vs. Transwarp StellarDB

System Properties Comparison Apache Kylin vs. Heroic vs. Microsoft Azure Synapse Analytics vs. Snowflake vs. Transwarp StellarDB

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

A distributed graph DBMS built for enterprise-level graph applications

Primary database model

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

Score	1.20
Rank	#175	Overall
	#80	Relational DBMS

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	0.07
Rank	#361	Overall
	#38	Graph DBMS

Website

kylin.apache.org

github.com/spotify/heroic

azure.microsoft.com/services/synapse-analytics

www.snowflake.com

www.transwarp.cn/en/product/stellardb

Technical documentation

kylin.apache.org/docs

spotify.github.io/heroic

docs.microsoft.com/azure/synapse-analytics

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

Spotify

Microsoft

Snowflake Computing Inc.

Transwarp

Initial release

2015

2014

2016

2014

Current release

3.1.0, July 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

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

Linux

hosted

Data scheme

yes

schema-free

yes

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

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

via Elasticsearch

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

OpenCypher

Supported programming languages

C#
Java
PHP

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Transact SQL

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

yes

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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.

User concepts

Access control

yes

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

yes

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