DBMS > Microsoft Azure AI Search vs. Microsoft SQL Server vs. NSDb vs. Spark SQL vs. Transbase

System Properties Comparison Microsoft Azure AI Search vs. Microsoft SQL Server vs. NSDb vs. Spark SQL vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Search-as-a-service for web and mobile app development

Microsofts flagship relational DBMS

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

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

Primary database model

Secondary database models

Vector DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	5.71
Rank	#64	Overall
	#8	Search engines

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

azure.microsoft.com/en-us/services/search

www.microsoft.com/en-us/sql-server

nsdb.io

spark.apache.org/sql

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

Technical documentation

learn.microsoft.com/en-us/azure/search

learn.microsoft.com/en-US/sql/sql-server

nsdb.io/Architecture

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

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

Developer

Microsoft

Apache Software Foundation

Transaction Software GmbH

Initial release

2015

1989

2017

2014

1987

Current release

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

restricted free version is available

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

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Implementation language

C++

Java, Scala

Scala

C and C++

Server operating systems

hosted

Linux
Windows

Linux
macOS

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

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.

yes

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

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

gRPC
HTTP REST
WebSocket

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C#
Java
JavaScript
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Java
Scala

Java
Python
R
Scala

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

Server-side scripts

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes, but depending on the SQL-Server Edition

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

Concurrency

Support for concurrent manipulation of data

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

yes

using Azure authentication

fine grained access rights according to SQL-standard

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