DBMS > ITTIA vs. NSDb vs. Spark SQL vs. TempoIQ vs. Transbase

System Properties Comparison ITTIA vs. NSDb vs. Spark SQL vs. TempoIQ vs. Transbase

Editorial information provided by DB-Engines

Name

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

Description

Edge Time Series DBMS with Real-Time Stream Processing

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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)

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

Primary database model

Secondary database models

Relational DBMS

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

Score	0.44
Rank	#267	Overall
	#23	Time Series DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

www.ittia.com

nsdb.io

spark.apache.org/sql

tempoiq.com (offline)

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

Technical documentation

nsdb.io/Architecture

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

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

Developer

ITTIA L.L.C.

Apache Software Foundation

TempoIQ

Transaction Software GmbH

Initial release

2007

2017

2014

2012

1987

Current release

8.7

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

free development license

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 and C++

Java, Scala

Scala

C and C++

Server operating systems

Linux
Windows

Linux
macOS

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

Fixed schema

yes

schema-free

yes

Typing

predefined data types such as float or date

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL-like query language

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API
WebSocket

gRPC
HTTP REST
WebSocket

JDBC
ODBC

HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C++
JavaScript (Node.js)
Python

Java
Scala

Java
Python
R
Scala

C#
Java
JavaScript

Node.js
Python
Ruby

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

Server-side scripts

Stored procedures

yes

Triggers

yes

Realtime Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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

Database file passwords

simple authentication-based access control

fine grained access rights according to SQL-standard

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