DBMS > Amazon Redshift vs. Apache Pinot vs. BigObject vs. Drizzle

System Properties Comparison Amazon Redshift vs. Apache Pinot vs. BigObject vs. Drizzle

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

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

Analytic DBMS for real-time computations and queries

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Website

aws.amazon.com/redshift

pinot.apache.org

bigobject.io

Technical documentation

docs.aws.amazon.com/redshift

docs.pinot.apache.org

docs.bigobject.io

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

BigObject, Inc.

Drizzle project, originally started by Brian Aker

Initial release

2012

2015

2008

Current release

1.0.0, September 2023

7.2.4, September 2012

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free community edition available

Open Source

GNU GPL

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++

Server operating systems

hosted

All OS with a Java JDK11 or higher

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

Data scheme

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

JDBC

fluentd
ODBC
RESTful HTTP API

JDBC

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
Python

C
C++
Java
PHP

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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