DBMS > Interbase vs. Microsoft Azure Table Storage vs. NSDb vs. Splice Machine

System Properties Comparison Interbase vs. Microsoft Azure Table Storage vs. NSDb vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Light-weight proven RDBMS

originally from Borland

A Wide Column Store for rapid development using massive semi-structured datasets

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

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

Primary database model

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

Score	4.08
Rank	#75	Overall
	#41	Relational DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

www.embarcadero.com/products/interbase

azure.microsoft.com/en-us/services/storage/tables

nsdb.io

splicemachine.com

Technical documentation

docs.embarcadero.com/products/interbase

nsdb.io/Architecture

splicemachine.com/how-it-works

Developer

Embarcadero

Microsoft

Splice Machine

Initial release

1984

2012

2017

2014

Current release

InterBase 2020, December 2019

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

AGPL 3.0, commercial license available

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

Java, Scala

Java

Server operating systems

Android
iOS
Linux
OS X
Windows

hosted

Linux
macOS

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

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.

export as XML data possible

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP API

gRPC
HTTP REST
WebSocket

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Scala

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

Server-side scripts

Stored procedures

yes

Interbase procedure and trigger language

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Interbase Change Views

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source 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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

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

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