DBMS > Databricks vs. Oracle vs. Riak TS vs. Teradata

System Properties Comparison Databricks vs. Oracle vs. Riak TS vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

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.

Widely used RDBMS

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

www.databricks.com

www.oracle.com/database

www.teradata.com

Technical documentation

docs.databricks.com

docs.oracle.com/en/database

www.tiot.jp/riak-docs/riak/ts/latest

docs.teradata.com

Developer

Databricks

Oracle

Open Source, formerly Basho Technologies

Teradata

Initial release

2013

1980

2015

1984

Current release

23c, September 2023

3.0.0, September 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

commercial

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 and C++

Erlang

Server operating systems

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
OS X

hosted
Linux

Data scheme

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

yes

Schemaless in JSON and XML columns

schema-free

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.

yes

Secondary indexes

yes

restricted

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

with Databricks SQL

yes

with proprietary extensions

yes, limited

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP API
Native Erlang Interface

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

Python
R
Scala

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

PL/SQL

also stored procedures in Java possible

Erlang

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

yes

Transaction concepts

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

ACID

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

User concepts

Access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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