DBMS > Amazon Redshift vs. Microsoft Azure Synapse Analytics vs. NSDb vs. OrientDB vs. Sphinx

System Properties Comparison Amazon Redshift vs. Microsoft Azure Synapse Analytics vs. NSDb vs. OrientDB vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

Multi-model DBMS (Document, Graph, Key/Value)

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store

Search engine

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

aws.amazon.com/redshift

azure.microsoft.com/services/synapse-analytics

nsdb.io

orientdb.org

sphinxsearch.com

Technical documentation

docs.aws.amazon.com/redshift

docs.microsoft.com/azure/synapse-analytics

nsdb.io/Architecture

www.orientdb.com/docs/last/index.html

sphinxsearch.com/docs

Developer

Amazon (based on PostgreSQL)

Microsoft

OrientDB LTD; CallidusCloud; SAP

Sphinx Technologies Inc.

Initial release

2012

2016

2017

2010

2001

Current release

3.2.29, March 2024

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache version 2

Open Source

GPL version 2, commercial licence 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

C++

Java, Scala

Java

C++

Server operating systems

hosted

Linux
macOS

All OS with a Java JDK (>= JDK 6)

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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.

Secondary indexes

restricted

yes

all fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language

SQL-like query language, no joins

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

gRPC
HTTP REST
WebSocket

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

C#
Java
PHP

Java
Scala

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Python

Transact SQL

Java, Javascript

Triggers

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

relationship in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

The original contents of fields are not stored in the Sphinx index.

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

yes

Access rights for users and roles; record level security configurable

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