DBMS > Apache IoTDB vs. Splice Machine vs. UniData,UniVerse

System Properties Comparison Apache IoTDB vs. Splice Machine vs. UniData,UniVerse

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

Time Series DBMS

Relational DBMS

Multivalue DBMS

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

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Score	3.47
Rank	#93	Overall
	#2	Multivalue DBMS

Website

iotdb.apache.org

splicemachine.com

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

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

splicemachine.com/how-it-works

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

Developer

Apache Software Foundation

Splice Machine

Rocket Software

Initial release

2018

2014

1985

Current release

1.1.0, April 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

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

All OS with a Java VM (>= 1.8)

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

optional

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 query language

yes

APIs and other access methods

JDBC
Native API

JDBC
Native Spark Datasource
ODBC

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

.Net
Basic

U2 Basic
C
Java

Server-side scripts

Stored procedures

yes

Java

yes

Triggers

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

yes

Access rights for users, groups and roles according to SQL-standard

Access rights according to SQL-standard and operating system based

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

TsFile: A Standard Format for IoT Time Series Data
27 February 2024, The New Stack

Linux 6.5 With AMD P-State EPP Default Brings Performance & Power Efficiency Benefits For Ryzen Servers
21 September 2023, Phoronix

AMD EPYC 8324P / 8324PN Siena 32-Core Siena Linux Server Performance Review
10 October 2023, Phoronix

IoTDB Provides Data Management for Industrial Edge IT
15 October 2020, The New Stack

Benchmarking The Performance Impact To AMD Inception Mitigations
15 August 2023, Phoronix

provided by Google News

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine ...
19 January 2021, PR Newswire

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

Distributed SQL System Review: Snowflake vs Splice Machine
18 September 2019, Towards Data Science

Splice Machine Hadoop RDBMS Integrated with RedPoint Solution to Deliver Big Data Digital Marketing Platform
7 April 2015, The Fast Mode

provided by Google News

UniData implements a milestone Smart Class Room project at The Asian School | THE DAILY TRIBUNE | KINGDOM ...
8 April 2024, News of Bahrain- DT News

Unidata uses Jetstream to make geoscience data available to science community
29 January 2020, IU Newsroom

Data + software services
21 August 2018, UCAR

Unidata turns 25 | NCAR & UCAR News
15 January 2010, news.ucar.edu

Bringing real weather to classrooms | NCAR & UCAR News
31 May 2012, news.ucar.edu

provided by Google News

Share this page