DBMS > Databricks vs. Immudb vs. Interbase vs. SAP HANA

System Properties Comparison Databricks vs. Immudb vs. Interbase vs. SAP HANA

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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.

An open source immutable (append-only) database with cryptographic verification which makes it tamper-resistant and fully auditable.

Light-weight proven RDBMS

originally from Borland

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Document store
Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
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	0.31
Rank	#295	Overall
	#42	Key-value stores

Score	4.08
Rank	#75	Overall
	#41	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

www.databricks.com

github.com/codenotary/immudb
immudb.io

www.embarcadero.com/products/interbase

www.sap.com/products/hana.html

Technical documentation

docs.databricks.com

docs.immudb.io

docs.embarcadero.com/products/interbase

help.sap.com/hana

Developer

Databricks

Codenotary

Embarcadero

SAP

Initial release

2013

2020

1984

2010

Current release

1.2.3, April 2022

InterBase 2020, December 2019

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Server operating systems

hosted

BSD
Linux
macOS
Solaris
Windows
z/OS

Android
iOS
Linux
OS X
Windows

Appliance or cloud-service

Data scheme

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

schema-free

yes

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

SQL-like syntax

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

gRPC protocol
PostgreSQL wire protocol

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Python
R
Scala

.Net
Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Interbase procedure and trigger language

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

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

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

yes

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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