DBMS > Databricks vs. dBASE vs. LMDB vs. RocksDB

System Properties Comparison Databricks vs. dBASE vs. LMDB vs. RocksDB

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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.

dBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.

A high performant, light-weight, embedded key-value database library

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store
Relational DBMS

Relational DBMS

Key-value store

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	9.70
Rank	#44	Overall
	#28	Relational DBMS

Score	2.09
Rank	#121	Overall
	#20	Key-value stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

www.databricks.com

www.dbase.com

www.symas.com/symas-embedded-database-lmdb

rocksdb.org

Technical documentation

docs.databricks.com

www.dbase.com/support/knowledgebase

www.lmdb.tech/doc

github.com/facebook/rocksdb/wiki

Developer

Databricks

Asthon Tate

Symas

Facebook, Inc.

Initial release

2013

1979

2011

2013

Current release

dBASE 2019, 2019

0.9.32, January 2024

9.2.1, May 2024

License

Commercial or Open Source

commercial

Open Source

BSD

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

DOS

dBase Classic
Windows

dBase Pro

Linux
Unix
Windows

Linux

Data scheme

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

yes

schema-free

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

SQL

Support of SQL

with Databricks SQL

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

none

The IDE can access other DBMS or ODBC-sources.

C++ API
Java API

Supported programming languages

Python
R
Scala

dBase proprietary IDE

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

The IDE can access stored procedures in other database systems.

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

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

not for dBase internal data, but IDE does support transactions when accessing external DBMS

ACID

yes

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

User concepts

Access control

Access rights for users and roles

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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