DBMS > Databricks vs. IBM Db2 warehouse vs. LevelDB vs. SQLite

System Properties Comparison Databricks vs. IBM Db2 warehouse vs. LevelDB vs. SQLite

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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.

Cloud-based data warehousing service

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Widely used embeddable, in-process RDBMS

Primary database model

Document store
Relational DBMS

Relational DBMS

Key-value store

Relational 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	1.37
Rank	#160	Overall
	#74	Relational DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

www.databricks.com

www.ibm.com/products/db2/warehouse

github.com/google/leveldb

www.sqlite.org

Technical documentation

docs.databricks.com

github.com/google/leveldb/blob/main/doc/index.md

www.sqlite.org/docs.html

Developer

Databricks

IBM

Google

Dwayne Richard Hipp

Initial release

2013

2014

2011

2000

Current release

1.23, February 2021

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Public Domain

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++

Server operating systems

hosted

Illumos
Linux
OS X
Windows

server-less

Data scheme

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

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

.NET Client API
JDBC
ODBC
OLE DB

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Python
R
Scala

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

PL/SQL, SQL PL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

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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