DBMS > Amazon Redshift vs. FeatureBase vs. Google Cloud Bigtable vs. Tkrzw

System Properties Comparison Amazon Redshift vs. FeatureBase vs. Google Cloud Bigtable vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS

Key-value store
Wide column store

Key-value store

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	0.23
Rank	#296	Overall
	#134	Relational DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	0.00
Rank	#385	Overall
	#61	Key-value stores

Website

aws.amazon.com/redshift

www.featurebase.com

cloud.google.com/bigtable

dbmx.net/tkrzw

Technical documentation

docs.aws.amazon.com/redshift

docs.featurebase.com

cloud.google.com/bigtable/docs

Developer

Amazon (based on PostgreSQL)

Molecula and Pilosa Open Source Contributors

Google

Mikio Hirabayashi

Initial release

2012

2017

2015

2020

Current release

2022, May 2022

0.9.3, August 2020

License

Commercial or Open Source

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

C++

Server operating systems

hosted

Linux
macOS

hosted

Linux
macOS

Data scheme

yes

schema-free

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL queries

APIs and other access methods

JDBC
ODBC

gRPC
JDBC
Kafka Connector
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Supported programming languages

All languages supporting JDBC/ODBC

Java
Python

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

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

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

yes

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

yes

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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