DBMS > Ehcache vs. Kyligence Enterprise vs. Microsoft Azure Synapse Analytics

System Properties Comparison Ehcache vs. Kyligence Enterprise vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

A distributed analytics engine for big data, built on top of Apache Kylin

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

Primary database model

Key-value store

Relational DBMS

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

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Website

www.ehcache.org

kyligence.io/kyligence-enterprise

azure.microsoft.com/services/synapse-analytics

Technical documentation

www.ehcache.org/documentation

docs.microsoft.com/azure/synapse-analytics

Developer

Terracotta Inc, owned by Software AG

Kyligence, Inc.

Microsoft

Initial release

2009

2016

Current release

3.10.0, March 2022

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses 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

All OS with a Java VM

Linux

hosted

Data scheme

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

JCache

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

Java

C#
Java
PHP

Server-side scripts

Stored procedures

Transact SQL

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

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

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

yes

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