DBMS > Amazon Redshift vs. FatDB vs. Google Cloud Datastore vs. InfluxDB vs. Oracle Berkeley DB

System Properties Comparison Amazon Redshift vs. FatDB vs. Google Cloud Datastore vs. InfluxDB vs. Oracle Berkeley DB

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Large scale data warehouse service for use with business intelligence tools

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

DBMS for storing time series, events and metrics

Widely used in-process key-value store

Primary database model

Relational DBMS

Document store
Key-value store

Document store

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Spatial DBMS

with GEO package

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Website

aws.amazon.com/redshift

cloud.google.com/datastore

www.influxdata.com/products/influxdb-overview

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/datastore/docs

docs.influxdata.com/influxdb

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Amazon (based on PostgreSQL)

FatCloud

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2012

2008

2013

1994

Current release

2.7.6, April 2024

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

commercial license available

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, C++ (depending on the Berkeley DB edition)

Server operating systems

hosted

Windows

hosted

Linux
OS X

through Homebrew

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

Numeric data and Strings

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

only with the Berkeley DB XML edition

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

Via inetgration in SQL Server

SQL-like query language (GQL)

SQL-like query language

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
ODBC

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP API
JSON over UDP

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

yes

via applications

using Google App Engine

Triggers

yes

via applications

Callbacks using the Google Apps Engine

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

in enterprise version only

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication using Paxos

selectable replication factor

in enterprise version only

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

Depending on used storage engine

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Can implement custom security layer via applications

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

simple rights management via user accounts

More information provided by the system vendor

Specific characteristics

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Key customers

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Market metrics

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Licensing and pricing models

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