DBMS > Oracle Coherence vs. ReductStore vs. Spark SQL vs. Transbase vs. Yanza

System Properties Comparison Oracle Coherence vs. ReductStore vs. Spark SQL vs. Transbase vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Oracles in-memory data grid solution

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Spark SQL is a component on top of 'Spark Core' for structured data processing

A resource-optimized, high-performance, universally applicable RDBMS

Time Series DBMS for IoT Applications

Primary database model

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

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

www.oracle.com/java/coherence

github.com/reductstore
www.reduct.store

spark.apache.org/sql

www.transaction.de/en/products/transbase.html

yanza.com

Technical documentation

docs.oracle.com/en/middleware/standalone/coherence

www.reduct.store/docs

spark.apache.org/docs/latest/sql-programming-guide.html

www.transaction.de/en/products/transbase/features.html

Developer

Oracle

ReductStore LLC

Apache Software Foundation

Transaction Software GmbH

Yanza

Initial release

2007

2023

2014

1987

2015

Current release

14.1, August 2023

1.9, March 2024

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Business Source License 1.1

Open Source

Apache 2.0

commercial

free development license

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++, Rust

Scala

C and C++

Server operating systems

All OS with a Java VM

Docker
Linux
macOS
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Windows

Data scheme

schema-free

yes

schema-free

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

SQL-like DML and DDL statements

yes

APIs and other access methods

JCache
JPA
RESTful HTTP API

HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

Supported programming languages

.Net
C++
Java

C++
JavaScript (Node.js)
Python
Rust

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

Triggers

yes

Live Events

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with selectable consistency level

none

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

configurable

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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

authentification to access the cache via certificates or http basic authentication

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Create custom Event Compression Policies in Enterprise Manager to reduce event noise
21 September 2023, blogs.oracle.com

Nike achieves scalability and performance with Oracle Coherence & Exadata
20 December 2012, blogs.oracle.com

New solutions for Oracle WebLogic on Azure Virtual Machines
1 April 2021, Microsoft

GridGain: Product Overview and Analysis
5 June 2019, eWeek

Hazelcast takes on proprietary Oracle in the In-Memory arena
17 June 2014, DatacenterDynamics

provided by Google News

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

Performant IPv4 Range Spark Joins | by Jean-Claude Cote
24 January 2024, Towards Data Science

18 Top Big Data Tools and Technologies to Know About in 2024
24 January 2024, TechTarget

provided by Google News

Share this page