DBMS > Amazon Redshift vs. Apache Druid vs. Bangdb vs. BigObject vs. EXASOL

System Properties Comparison Amazon Redshift vs. Apache Druid vs. Bangdb vs. BigObject vs. EXASOL

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Converged and high performance database for device data, events, time series, document and graph

Analytic DBMS for real-time computations and queries

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

docs.bangdb.com

docs.bigobject.io

www.exasol.com/resources

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Sachin Sinha, BangDB

BigObject, Inc.

Exasol

Initial release

2012

2015

2000

Current release

29.0.1, April 2024

BangDB 2.0, October 2021

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

BSD 3

commercial

free community edition available

commercial

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, C++

Server operating systems

hosted

Linux
OS X
Unix

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Data scheme

yes

schema-less columns are supported

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

SQL like support with command line tool

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

Proprietary protocol
RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Java
Python

Java
Lua
Python
R

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

user defined functions

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

selectable replication factor, Knob for CAP (enterprise version only)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

none

Immediate Consistency

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

yes, optimistic concurrency control

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes

yes, run db with in-memory only mode

yes

User concepts

Access control

fine grained access rights according to SQL-standard

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

yes (enterprise version only)

Access rights for users, groups and roles according to SQL-standard

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