DBMS > Amazon Redshift vs. Apache Pinot vs. Google Cloud Bigtable vs. Oracle Berkeley DB

System Properties Comparison Amazon Redshift vs. Apache Pinot vs. Google Cloud Bigtable vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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

Widely used in-process key-value store

Primary database model

Relational DBMS

Key-value store
Wide column store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

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

Website

aws.amazon.com/redshift

pinot.apache.org

cloud.google.com/bigtable

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

Technical documentation

docs.aws.amazon.com/redshift

docs.pinot.apache.org

cloud.google.com/bigtable/docs

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

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2012

2015

1994

Current release

1.0.0, September 2023

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

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

Server operating systems

hosted

All OS with a Java JDK11 or higher

hosted

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

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

SQL-like query language

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
ODBC

JDBC

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

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
Python

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

.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

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

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

Source-replica replication

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)

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Atomic single-row operations

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

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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