DBMS > Amazon Redshift vs. BigObject vs. Databricks vs. Datomic vs. RethinkDB

System Properties Comparison Amazon Redshift vs. BigObject vs. Databricks vs. Datomic vs. RethinkDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for real-time computations and queries

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Relational DBMS

Relational DBMS

Document store

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	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Databricks

Cognitect

The Linux Foundation

since July 2017

Initial release

2012

2015

2013

2012

2009

Current release

1.0.6735, June 2023

2.4.1, August 2020

License

Commercial or Open Source

commercial

free community edition available

commercial

limited edition free

Open Source

Apache Version 2

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

Java, Clojure

C++

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

with Databricks SQL

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

Clojure
Java

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

user defined functions and aggregates

yes

Transaction Functions

Triggers

By using transaction functions

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

none

But extensive use of caching in the application peers

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

none

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

Transaction concepts

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

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

MVCC based

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

User concepts

Access control

fine grained access rights according to SQL-standard

yes

users and table-level permissions

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.