DBMS > Amazon Redshift vs. Ignite vs. Qdrant vs. Sadas Engine vs. Teradata

System Properties Comparison Amazon Redshift vs. Ignite vs. Qdrant vs. Sadas Engine vs. Teradata

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

A high-performance vector database with neural network or semantic-based matching

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Vector DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

aws.amazon.com/redshift

ignite.apache.org

github.com/qdrant/qdrant
qdrant.tech

www.sadasengine.com

www.teradata.com

Technical documentation

docs.aws.amazon.com/redshift

apacheignite.readme.io/docs

qdrant.tech/documentation

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.teradata.com

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Qdrant

SADAS s.r.l.

Teradata

Initial release

2012

2015

2021

2006

1984

Current release

Apache Ignite 2.6

8.0

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

free trial version available

commercial

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, .Net

Rust

C++

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Docker
Linux
macOS
Windows

AIX
Linux
Windows

hosted
Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

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

restricted

yes

Keywords, numberic ranges, geo, full-text

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

does not fully support an SQL-standard

ANSI-99 for query and DML statements, subset of DDL

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
ODBC

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

JDBC
ODBC
Proprietary protocol

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

.Net
C
C#
C++
Groovy
Java
PHP
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes (compute grid and cache interceptors can be used instead)

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

Collection-level replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency, tunable consistency

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

In-memory capabilities

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

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Security Hooks for custom implementations

Key-based authentication

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

fine grained access rights according to SQL-standard

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