DBMS > Databricks vs. Interbase vs. LeanXcale vs. RocksDB

System Properties Comparison Databricks vs. Interbase vs. LeanXcale 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.

Light-weight proven RDBMS

originally from Borland

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

Primary database model

Document store
Relational DBMS

Relational DBMS

Key-value store
Relational DBMS

Key-value store

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	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

www.databricks.com

www.embarcadero.com/products/interbase

www.leanxcale.com

rocksdb.org

Technical documentation

docs.databricks.com

docs.embarcadero.com/products/interbase

github.com/facebook/rocksdb/wiki

Developer

Databricks

Embarcadero

LeanXcale

Facebook, Inc.

Initial release

2013

1984

2015

2013

Current release

InterBase 2020, December 2019

8.11.4, April 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

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Implementation language

C++

Server operating systems

hosted

Android
iOS
Linux
OS X
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

export as XML data possible

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

through Apache Derby

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

C++ API
Java API

Supported programming languages

Python
R
Scala

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

C
Java
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Interbase procedure and trigger language

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Interbase Change Views

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

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

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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