DBMS > Amazon Redshift vs. Apache Impala vs. Infobright vs. ScyllaDB vs. Valentina Server

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Infobright vs. ScyllaDB vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Cassandra and DynamoDB compatible wide column store

Object-relational database and reports server

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Document store

Key-value store

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

aws.amazon.com/redshift

impala.apache.org

ignitetech.com/softwarelibrary/infobrightdb

www.scylladb.com

www.valentina-db.net

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

docs.scylladb.com

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Ignite Technologies Inc.; formerly InfoBright Inc.

ScyllaDB

Paradigma Software

Initial release

2012

2013

2005

2015

1999

Current release

4.1.0, June 2022

ScyllaDB Open Source 5.4.1, January 2024

5.7.5

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

Open Source (AGPL), commercial license 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.

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Implementation language

C++

Server operating systems

hosted

Linux

Linux
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

Knowledge Grid Technology used instead

yes

cluster global secondary indices

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

SQL-like DML and DDL statements (CQL)

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

yes, Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Source-replica replication

selectable replication factor

Representation of geographical distribution of servers is possible

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

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

Atomicity and isolation are supported for single operations

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

in-memory tables

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

Access rights for users can be defined per object

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
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