DBMS > Apache Kylin vs. DolphinDB vs. Microsoft Azure Synapse Analytics vs. Snowflake

System Properties Comparison Apache Kylin vs. DolphinDB vs. Microsoft Azure Synapse Analytics vs. Snowflake

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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

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

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

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

kylin.apache.org

www.dolphindb.com

azure.microsoft.com/services/synapse-analytics

www.snowflake.com

Technical documentation

kylin.apache.org/docs

docs.dolphindb.cn/en/help200/index.html

docs.microsoft.com/azure/synapse-analytics

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

DolphinDB, Inc

Microsoft

Snowflake Computing Inc.

Initial release

2015

2018

2016

2014

Current release

3.1.0, July 2020

v2.00.4, January 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community version available

commercial

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

hosted

Data scheme

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

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

C#
Java
PHP

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Transact SQL

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding, horizontal partitioning

yes

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

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

yes

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

Administrators, Users, Groups

yes

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

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