DBMS > Apache Kylin vs. Hive vs. Microsoft Azure Synapse Analytics vs. Quasardb vs. Riak KV

System Properties Comparison Apache Kylin vs. Hive vs. Microsoft Azure Synapse Analytics vs. Quasardb vs. Riak KV

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Distributed, high-performance timeseries database

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

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

Score	1.28
Rank	#155	Overall
	#74	Relational DBMS

Score	78.66
Rank	#17	Overall
	#11	Relational DBMS

Score	22.46
Rank	#36	Overall
	#22	Relational DBMS

Score	0.07
Rank	#330	Overall
	#29	Time Series DBMS

Score	6.29
Rank	#69	Overall
	#10	Key-value stores

Website

kylin.apache.org

hive.apache.org

azure.microsoft.com/services/synapse-analytics

quasardb.net

riak.com/products/riak-kv

Technical documentation

kylin.apache.org/docs

cwiki.apache.org/confluence/display/Hive/Home

docs.microsoft.com/azure/synapse-analytics

doc.quasar.ai/master

Developer

Apache Software Foundation, originally contributed from eBay Inc

Apache Software Foundation

initially developed by Facebook

Microsoft

quasardb

Basho Technologies

Initial release

2015

2012

2016

2009

Current release

3.1.0, July 2020

3.1.2, August 2019

3.13.3, April 2022

2.1.0, April 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache version 2, commercial enterprise edition

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++

Erlang

Server operating systems

Linux

All OS with a Java VM

hosted

BSD
Linux
OS X
Windows

Linux
OS X

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

restricted

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

SQL-like DML and DDL statements

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC

HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

C++
Java
PHP
Python

C#
Java
PHP

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Transact SQL

JavaScript and Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

consistent hashing

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Source-replica replication with selectable replication factor

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

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

links between data sets can be stored

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

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

Access rights for users, groups and roles

yes

Cryptographically strong user authentication and audit trail

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