DBMS > Amazon Redshift vs. Quasardb vs. Realm vs. SAP HANA vs. TimescaleDB

System Properties Comparison Amazon Redshift vs. Quasardb vs. Realm vs. SAP HANA vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Distributed, high-performance timeseries database

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

In-memory, column based data store. Available as appliance or cloud service

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

aws.amazon.com/redshift

quasar.ai

realm.io

www.sap.com/products/hana.html

www.timescale.com

Technical documentation

docs.aws.amazon.com/redshift

Developer

Amazon (based on PostgreSQL)

quasardb

Realm, acquired by MongoDB in May 2019

SAP

Timescale

Initial release

2012

2009

2014

2010

2017

Current release

3.14.1, January 2024

2.0 SPS07 (April 4, 2023), April 2023

2.15.0, May 2024

License

Commercial or Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

hosted

BSD
Linux
OS X
Windows

Android
Backend: server-less
iOS
Windows

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

restricted

yes

with tags

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
ODBC

HTTP API

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
Java

with Android only
Objective-C
React Native
Swift

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

runs within the applications so server-side scripts are unnecessary

SQLScript, R

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

none

yes

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication with selectable replication factor

none

yes

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

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

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

In-Memory realm

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

yes

fine grained access rights according to SQL-standard

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