DBMS > Databricks vs. Microsoft Access vs. Microsoft Azure Table Storage vs. Oracle vs. Prometheus

System Properties Comparison Databricks vs. Microsoft Access vs. Microsoft Azure Table Storage vs. Oracle vs. Prometheus

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.

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used RDBMS

Open-source Time Series DBMS and monitoring system

Primary database model

Document store
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

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

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

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

www.databricks.com

www.microsoft.com/en-us/microsoft-365/access

azure.microsoft.com/en-us/services/storage/tables

www.oracle.com/database

prometheus.io

Technical documentation

docs.databricks.com

developer.microsoft.com/en-us/access

docs.oracle.com/en/database

prometheus.io/docs

Developer

Databricks

Microsoft

Oracle

Initial release

2013

1992

2012

1980

2015

Current release

1902 (16.0.11328.20222), March 2019

23c, September 2023

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

Server operating systems

hosted

Windows

Not a real database server, but making use of DLLs

hosted

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

Linux
Windows

Data scheme

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

yes

schema-free

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

but not compliant to any SQL standard

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

RESTful HTTP/JSON API

Supported programming languages

Python
R
Scala

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

since Access 2010 using the ACE-engine

PL/SQL

also stored procedures in Java possible

Triggers

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication
Source-replica replication

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

but no files for transaction logging

optimistic locking

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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'

User concepts

Access control

a simple user-level security was built in till version Access 2003

Access rights based on private key authentication or shared access signatures

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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