DBMS > Apache Kylin vs. Dolt vs. Microsoft Azure Synapse Analytics vs. Riak TS vs. Snowflake

System Properties Comparison Apache Kylin vs. Dolt vs. Microsoft Azure Synapse Analytics vs. Riak TS vs. Snowflake

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 MySQL compatible DBMS with Git-like versioning of data and schema

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

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

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store

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

Score	1.20
Rank	#175	Overall
	#80	Relational DBMS

Score	0.95
Rank	#197	Overall
	#93	Relational DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Score	0.25
Rank	#308	Overall
	#26	Time Series DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

kylin.apache.org

github.com/dolthub/dolt
www.dolthub.com

azure.microsoft.com/services/synapse-analytics

www.snowflake.com

Technical documentation

kylin.apache.org/docs

docs.dolthub.com

docs.microsoft.com/azure/synapse-analytics

www.tiot.jp/riak-docs/riak/ts/latest

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

DoltHub Inc

Microsoft

Open Source, formerly Basho Technologies

Snowflake Computing Inc.

Initial release

2015

2018

2016

2015

2014

Current release

3.1.0, July 2020

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

Open Source

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++

Erlang

Server operating systems

Linux

Linux
macOS
Windows

hosted

Linux
OS X

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

restricted

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

yes, limited

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

CLI Client
HTTP REST

ADO.NET
JDBC
ODBC

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
Java
PHP

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

currently in alpha release

Transact SQL

Erlang

user defined functions

Triggers

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

yes

selectable replication factor

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

Foreign keys

Referential integrity

yes

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

links between datasets can be stored

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

Only one user is configurable, and must be specified in the config file at startup

yes

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

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