DBMS > Apache Kylin vs. jBASE vs. Microsoft Azure Synapse Analytics vs. Snowflake vs. Vertica

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

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 robust multi-value DBMS comprising development tools and middleware

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Multivalue DBMS

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

kylin.apache.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

azure.microsoft.com/services/synapse-analytics

www.snowflake.com

www.vertica.com

Technical documentation

kylin.apache.org/docs

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

docs.microsoft.com/azure/synapse-analytics

docs.snowflake.net/manuals/index.html

vertica.com/documentation

Developer

Apache Software Foundation, originally contributed from eBay Inc

Rocket Software (formerly Zumasys)

Microsoft

Snowflake Computing Inc.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2015

1991

2016

2014

2005

Current release

3.1.0, July 2020

5.7

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

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

AIX
Linux
Windows

hosted

Linux

Data scheme

yes

schema-free

yes

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

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

optional

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

Embedded SQL for jBASE in BASIC

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

.Net
Basic
Jabbascript
Java

C#
Java
PHP

JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes

Transact SQL

user defined functions

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

similar concept for controling cloud resources

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

yes

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

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

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

Access rights can be defined down to the item level

yes

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

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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