DBMS > FatDB vs. ReductStore vs. Spark SQL vs. TempoIQ vs. Yaacomo

System Properties Comparison FatDB vs. ReductStore vs. Spark SQL vs. TempoIQ vs. Yaacomo

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store
Key-value store

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/reductstore
www.reduct.store

spark.apache.org/sql

tempoiq.com (offline)

yaacomo.com

Technical documentation

www.reduct.store/docs

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

Developer

FatCloud

ReductStore LLC

Apache Software Foundation

TempoIQ

Q2WEB GmbH

Initial release

2012

2023

2014

2012

2009

Current release

1.9, March 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Business Source License 1.1

Open Source

Apache 2.0

commercial

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

C++, Rust

Scala

Server operating systems

Windows

Docker
Linux
macOS
Windows

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

schema-free

yes

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

Via inetgration in SQL Server

SQL-like DML and DDL statements

yes

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

HTTP API

JDBC
ODBC

HTTP API

JDBC
ODBC

Supported programming languages

C++
JavaScript (Node.js)
Python
Rust

Java
Python
R
Scala

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

yes

via applications

Triggers

yes

via applications

yes

Realtime Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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

Can implement custom security layer via applications

simple authentication-based access control

fine grained access rights according to SQL-standard

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