DBMS > Databricks vs. jBASE vs. Oracle vs. Postgres-XL vs. Splice Machine

System Properties Comparison Databricks vs. jBASE vs. Oracle vs. Postgres-XL vs. Splice Machine

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.

A robust multi-value DBMS comprising development tools and middleware

Widely used RDBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store
Relational DBMS

Multivalue 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
Spatial 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	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

www.databricks.com

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.oracle.com/database

www.postgres-xl.org

splicemachine.com

Technical documentation

docs.databricks.com

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

docs.oracle.com/en/database

www.postgres-xl.org/documentation

splicemachine.com/how-it-works

Developer

Databricks

Rocket Software (formerly Zumasys)

Oracle

Splice Machine

Initial release

2013

1991

1980

2014

since 2012, originally named StormDB

2014

Current release

5.7

23c, September 2023

10 R1, October 2018

3.1, March 2021

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Mozilla public license

Open Source

AGPL 3.0, commercial license available

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++

Java

Server operating systems

hosted

AIX
Linux
Windows

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

Linux
macOS

Linux
OS X
Solaris
Windows

Data scheme

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

schema-free

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

optional

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

Embedded SQL for jBASE in BASIC

yes

with proprietary extensions

yes

distributed, parallel query execution

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Python
R
Scala

.Net
Basic
Jabbascript
Java

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

PL/SQL

also stored procedures in Java possible

user defined functions

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

isolation level can be parameterized

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

Access rights for users, groups and roles 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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