DBMS > Amazon Redshift vs. Datomic vs. InfluxDB vs. Microsoft Azure Synapse Analytics vs. NSDb

System Properties Comparison Amazon Redshift vs. Datomic vs. InfluxDB vs. Microsoft Azure Synapse Analytics vs. NSDb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

DBMS for storing time series, events and metrics

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

Primary database model

Secondary database models

Spatial DBMS

with GEO package

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	26.56
Rank	#28	Overall
	#1	Time Series DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Website

aws.amazon.com/redshift

www.datomic.com

www.influxdata.com/products/influxdb-overview

azure.microsoft.com/services/synapse-analytics

nsdb.io

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

docs.influxdata.com/influxdb

docs.microsoft.com/azure/synapse-analytics

nsdb.io/Architecture

Developer

Amazon (based on PostgreSQL)

Cognitect

Microsoft

Initial release

2012

2013

2016

2017

Current release

1.0.6735, June 2023

2.7.5, January 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

MIT-License; commercial enterprise version available

commercial

Open Source

Apache Version 2.0

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, Clojure

C++

Java, Scala

Server operating systems

hosted

All OS with a Java VM

Linux
OS X

through Homebrew

hosted

Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data and Strings

yes

yes: int, bigint, decimal, string

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

HTTP API
JSON over UDP

ADO.NET
JDBC
ODBC

gRPC
HTTP REST
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

C#
Java
PHP

Java
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

Transact SQL

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

in enterprise version only

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

selectable replication factor

in enterprise version only

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

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 external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

Using Apache Lucene

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

Depending on used storage engine

User concepts

Access control

fine grained access rights according to SQL-standard

simple rights management via user accounts

yes

More information provided by the system vendor

Specific characteristics

InfluxData is the creator of InfluxDB , the open source time series database. It...
» more

Competitive advantages

Time to Value InfluxDB is available in all the popular languages and frameworks,...
» more

Typical application scenarios

IoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» more

Key customers

InfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
» more

Market metrics

Fastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
» more

Licensing and pricing models

Open source core with closed source clustering available either on-premise or on...
» more

News

Sync Data from InfluxDB v2 to v3 With the Quix Template
8 April 2024

Infrastructure Monitoring Basics: Getting Started with Telegraf, InfluxDB, and Grafana
5 April 2024

Comparing Dates in Java: A Tutorial
3 April 2024

Python ARIMA Tutorial
29 March 2024

Time Series, InfluxDB, and Vector Databases
26 March 2024

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.