DBMS > FeatureBase vs. Splice Machine vs. Valentina Server vs. Yaacomo

System Properties Comparison FeatureBase vs. Splice Machine vs. Valentina Server vs. Yaacomo

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Editorial information provided by DB-Engines

Name

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

Description

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Object-relational database and reports server

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

Primary database model

Relational DBMS

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

Score	0.22
Rank	#302	Overall
	#136	Relational DBMS

Score	0.54
Rank	#242	Overall
	#113	Relational DBMS

Score	0.16
Rank	#319	Overall
	#144	Relational DBMS

Website

www.featurebase.com

splicemachine.com

www.valentina-db.net

yaacomo.com

Technical documentation

docs.featurebase.com

splicemachine.com/how-it-works

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Molecula and Pilosa Open Source Contributors

Splice Machine

Paradigma Software

Q2WEB GmbH

Initial release

2017

2014

1999

2009

Current release

2022, May 2022

3.1, March 2021

5.7.5

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Server operating systems

Linux
macOS

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

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

SQL queries

yes

APIs and other access methods

gRPC
JDBC
Kafka Connector
ODBC

JDBC
Native Spark Datasource
ODBC

ODBC

JDBC
ODBC

Supported programming languages

Java
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes, using Linux fsync

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 for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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